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The Ultimate Guide to Credit Risk Analytics: Benefits and Pitfalls of Microservices

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The Ultimate Guide to Credit Risk Analytics:
Benefits and Pitfalls of Microservices

What is Credit Risk Analytics?

Credit risk analytics refers to the process of assessing the probability of default by borrowers and measuring the potential losses that lenders may incur due to credit defaults. This involves analyzing various factors such as the borrower’s credit history, financial health, and other relevant metrics to determine their creditworthiness.

Financial institutions rely heavily on credit risk analytics to make informed lending decisions and manage their credit risk exposure. By analyzing credit risk, financial institutions can identify potential losses and take proactive measures to minimize them. This can include measures such as setting appropriate interest rates, requiring collateral or guarantees, and establishing risk management policies and procedures.

However, relying solely on credit risk analytics without appropriate risk management solutions can have severe consequences. For example, if a lender fails to identify and mitigate potential credit risks, it could lead to significant losses, which could ultimately impact the lender’s financial stability and reputation. Therefore, it is essential for financial institutions to have robust risk management policies and procedures in place to manage potential credit risks effectively.

How is Credit risk management software used for risk analytics?

Credit risk management software is a specialized tool used by financial institutions to assess and manage the risk of default by borrowers. It leverages advanced analytics and modeling techniques to provide a comprehensive assessment of the creditworthiness of a borrower, helping lenders make informed decisions regarding loan approvals, interest rates, and other terms and conditions. Credit risk management software can also monitor and analyze credit portfolios, providing ongoing risk assessments and alerts to potential issues. By automating much of the credit risk management process, this software empowers financial institutions to improve the accuracy and efficiency of their risk management practices, ultimately leading to better business outcomes.

What does this mean for programmers?

As we speak, thousands of programmers across the globe are frantically fielding error messages and digging through millions of lines of code to prop up whatever development misstep threatens potential risks of a data breach that could annihilate their respective universe at any given time. Phew. Every so often, a new concept enters the fray, a prophet peddling the hope of a better future. More recently, this future comes in the form of a microservices architecture – the fulfillment of Service Oriented Architecture’s (SOA) loosely coupled promise.

To give you a deeper understanding of what this means for programmers, we created this ultimate risk analytics guide filled with valuable insights to aid in your risk assessment process. As an organization that gets input from a variety of risk management professionals and multiple sources, we see the benefits of using microservices in financial institutions, the positives and pitfalls of implementing them for the long-haul, and the differences between them and the traditional, monolithic approach to development.

Microservices: Risk analytics solutions

When assessing risk and development blunders, programmers everywhere say, “Everything is breaking, always.” However, this is not a catastrophe; this is the fragile reality of software development – every enterprise is a house of cards mortared with sticky notes and energy drinks.

On occasion, brand new concepts will crop up and give said programmers the potential to alleviate key risk indicators. The newest concept in question is the use of microservices. Together, we’ll explore their role in managing risk and increasing business performance for software developers globally.

So, if you’re thinking about making the move to microservices, keep reading.

3 Benefits of Microservices in Risk Management

A good place to start is to understand three high-level benefits that have propelled the adoption of microservices and the role they play in managing risk analytics solutions.

  • Microservices are Agile

    Let me paint this as a story. A hypothetical Chief Risk Officer would have their team expose a scorecard as a service as part of the credit underwriting process. The Chief Operations Officer is in charge of that process. In context of the exposure of the scorecard, everyone agrees that if the underwriting process passes seven variables to the scorecard service, the service will return as a score.

    As long as I don’t violate the contract – give me seven data points, and I’ll return a score – it doesn’t matter to the underwriting process how the score is calculated. If the risk management team discovers new data analytics data sources they can leverage, or if a new scoring model is created, they are free to implement; that change will not negatively impact the underwriting process. This level of agility means risk professionals can quickly adapt to a changing risk factors landscape.

  • Microservices are Resilient

    You have heard that because a microservice is autonomous and loosely coupled, the failure of one service tends to happen in isolation of the rest of the system. In the example above, as long as the service that is exposed adheres to the original contract, the processes that rely on the service will not break. Both sides of the contract – give me seven variables and I will give you a score – are able to meet terms in the contract best. The underwriting process can retrieve the variables any way deemed best, and the scorecard service can calculate the score as deemed best. As long as the contract is honored, neither is impacted.

  • Microservices are Open

    At this point, most microservices are designed to leverage REST as the mechanism for data exchange. REST has shown itself to be secure, lightweight, and flexible. This open nature represents enormous potential in the creation of end-to-end processes to meet operational needs of the enterprise.

    Now that you’ve got a feel for why microservices could mean a better future for software developers, it’s essential for risk managers to learn the advantages and disadvantages of using them for the long haul.

Risk Analytics in a Microservices Architecture: The positives and pitfalls of microservices in the long-term

The agile, resilient, and open nature of a microservices architecture are all significant benefits you get at first sight, but nothing is perfect. What about the long haul?

This Q&A goes further into the implementation of microservices, and some of the long-term positives and pitfalls.

  • How have microservices changed application development?

    The vision to create a loosely-coupled enterprise environment has been a Holy Grail for some time. While the same theories and techniques showed promise with XML and SOAP-based web services, the implementation of microservices better supports an agile approach to development. The decomposition of monolithic end-to-end processes gives product and process designers and developers the flexibility to create solutions that may be better fit for purpose. It enables these professionals to define more discrete capabilities, allowing developers to develop discrete functions – a more appropriate solution to the business problem they must solve.

  • What are the most common risk issues you see affecting the implementation of microservices?

    Microservices are yet another operational and developmental paradigm shift. These shifts always present challenges to implementation. The architectural maturity of an organization is often the most significant hindrance to adoption and implementation. If an organization is not in a place to facilitate the exposure of microservices, for example, due to legacy systems not supporting open messaging, it will hinder implementation.

  • Do you have any concerns regarding the current state of microservices?

    My biggest concern regarding the state of microservices is the possibility that an organization may not adequately secure its endpoints. Due to the lightweight nature of microservices, it is not a prescriptive technology. By contrast, SOAP is governed by a standards body that ensures prescriptive security recommendations are provided. Microservices are not governed, so the potential roll-out is very “wild west.”

  • What kind of security techniques and tools do you find most effective for securing microservices?

    The efficacy of security techniques and tools depend on the environment into which the microservice is deployed, but let’s take a general perspective. Microservices do not lend themselves to the “traditional” mode of security because components are not conjoined, therefore they do not share access to a common data repository (think identity control). To avoid making calls to an authentication service in every instance, using OAuth (Open Authorization) as a delegation protocol can simultaneously ensure the security and agility of the system.  

  • What do developers need to keep in mind when working on microservices?

    When working on microservices, developers must be simple and discrete. A service should not be complicated. It should solve one singular problem. It should be as simple as: Give me seven data points, and I will give you a score. Nothing more.

  • What’s the future for microservices – where do the greatest opportunities lie?

    One of the greatest opportunities in microservices lies in the potential for reuse. For example, many organizations require the ability to quickly reference employee information to match skill level to a given task. Instead of writing the code to look-up required information every time it is used in a process, the organization could write an employee look-up service to be reused by any process that needs the information.

  • Which programming languages, frameworks, and tools does Provenir use to enable the creation of microservices?

    Provenir implements a development technique called graph theory, rather than implementing a language like Java or Scala. Graphs are designed and developed using Provenir’s Studio and deployed to our Decision Engine. As part of the development, users can expose REST-based endpoints. These endpoints enable decisions, analytics, processes, etc., to be exposed as microservices. We also provide tools that enable the testing and documentation of the exposed services.

    To gain a better understanding of the concept as a whole, it’s important to nail the basics. In the final part of this risk analytics guide, we deep-dive into the defining differences of microservices and the traditional monolith and how they contribute to risk management strategies.

Microservices vs. Monolith

Unless you’ve been living under a rock without wi-fi, in which case I would question your ability to read this article, you’ve likely heard the concept of microservices compared with a monolithic architectural style. Comparison with the monolith is a great way to explain the characteristics of a microservices style because the two architectural concepts exist in stark contrast: large and interwoven, small and discrete.

For this section of the guide, we’ll contrast microservices with the monolithic approach to development to gain a baseline understanding of the concept.

  • Microservices

    Part of an architectural concept where the focus is on discrete services that do one thing and do that one thing very well. In the risk decisioning context an analytics group within an organization might be responsible for developing and exposing scorecards as microservices. 

    The data scientist, or analysts, would focus on developing really good scorecards and making sure these scorecards continuously deliver quality results. They would then expose these scorecards as discrete services that could be called upon to deliver excellent and accurate scores. An operations group could then develop applications or processes that call out to these scorecards at the right time, leveraging these scores in a decision process.

  • The Monolith

    Most of the time, business processes are designed to be an end-to-end process. That’s what we call a monolithic architecture. All parts of the decision process are developed as one, large complex process. Let’s consider scorecards again, as an example. If you want to make a change to a scorecard there may be a great deal of coordination, refactoring or redevelopment of the process, then testing before rolling out again.

  • What’s Next?

    Now that we’ve gone in dept on microservices… what’s next? Where is the industry headed? The use of microservices in financial technology can simplify how you turn your scorecards, risk models and other analytics components into services for use in a loan origination and decisioning processes. Simple right? But don’t forget that having the foundation of the right scorecards, data and risk models is critical. And then if you want to implement advanced analytics like AI/ML, you may be looking at additional challenges, despite the vast improvements it offers across the modeling lifecycle.

    For more information on how to implement advanced AI algorithms (and maybe inform even more powerful microservices?), continue reading here.

Need to balance your credit risk analytics and management with speed and business growth?

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Myths vs reality in upgrading your credit decisioning technology

Myths vs Reality in Upgrading Your Credit Decisioning Technology

Powering Up: How Banks Can Leverage Automated Credit Risk Decisioning for More Agility and Speed

Financial institutions are under pressure, and banks are feeling the heat. Consumers are even more resistant to friction in their customer experience journeys, whether they are buying appliances, vacations, vehicles, or applying for credit. So how can banks focus on growth and meeting consumer needs and expectations, while still managing risk effectively? In many cases, it means it’s time to look at your data and decisioning technology. 

Upgrading your credit risk decisioning technology sounds daunting. But we’re here to talk about some of the myths that persist around upgrading your tech – and the reality counterpoint. 

Myth #1:
Traditional Credit Data is Good Enough

Reality:
Traditional credit data is rarely enough to paint an accurate, holistic picture of a customers’ creditworthiness. Alternative data sources, including mobile/telco info, rent and utilities data, social media/web presence, and open banking info can help you gain a more comprehensive view of a potential customers’ financial health as well as their ability and willingness to pay.

The Data Challenge:
There is a ton of data out there, and it can often reside in siloed environments, making it difficult to access and costly to integrate into your decisioning. On top of that, it can be easy to assume that more data is the answer. But it’s not always what you need. The key to optimizing your data strategy is not necessarily more data but having the right data at the right time. According to IDC, in 2022“over one hundred thousand exabytes of data will [have been] generated, crossing the 100k threshold for the first time.” Yet 74% of decision-makers we surveyed said they struggle with their organization’s credit risk strategy because data is not easily accessible, and 70% say alternative data is not easily integrated into their current decisioning system. The use of alternative data to supplement traditional credit data (primarily bureau data) is critical to not only giving you a more accurate, real-time view of your customers’ creditworthiness, but it also expands your lendable market. By being more inclusive and saying yes to individuals who may have lower traditional credit scores, you’re improving financial inclusion and ensuring greater access to financial services andgrowing your business at the same time.

Myth #2:
It’s Too Costly to Upgrade Your Decisioning Tech

Reality:
It can be easy to assume that changing your decisioning tech will involve a massive amount of upfront investment (not to mention the fear of ‘wasting’ previous investments in your legacy tech). But can you afford not to upgrade? And keep in mind additional cost savings realized with self-sufficiency when changing your decisioning workflows and launching new products.

The Cost Challenge:
Cost pressures are everywhere. So it’s not surprising that sometimes banks are reluctant to consider changing technology platforms. With the hours of time and monetary investments made in implementing decisioning infrastructure, it can seem wasteful to transition away from legacy systems. But it’s important not to let the fear of past investments hold you back. Because with increased competition, demanding consumer expectations, and a shifting regulatory environment, having next generation decisioning tech is key. The cost of doing nothing will catch up to you – acquiring new customers, keeping your existing customers, preventing fraud, satisfying compliance requirements… non-action is a non-option. Upgrading your decisioning tech results in a lower total cost of ownership, thanks to eliminating product launch and iteration delays that lose you customers, the ability to automate risk decisioning workflows for more efficient processes, and improved fraud detection/prevention.

Myth #3:
It’s Too Difficult to Overhaul our Current Systems

Reality:
It’s not an all-or-nothing situation. Look for decisioning solutions that can run in parallel to your current software, or for ways to orchestrate your data more efficiently with a data ecosystem. This can create buy-in with other departments and lines of business when they see the improved efficiency and the way upgraded tech improves the overall decisioning process.

The Difficulty Challenge:
We’ve talked about the cost aspect of upgrading, which sounds daunting, but it’s about more than just money. Many people-hours are often put into choosing and implementing decisioning platforms – so why opt to do it all over again? Because the long-term benefits are worth it, and it may not be as difficult as it sounds. Rarely do you need to rip and replace all of your decisioning tech in one go. There are more flexible, agile decisioning platforms available that can integrate into or run alongside your existing workflows or you can choose to upgrade one line of business at a time. The key is choosing a technology platform that makes this easy and has experience with swapping out competitive decisioning platforms. (Provenir for example has vast amounts of experience replacing legacy, competitive decisioning systems, and can get you up and running, fast – however large the implementation may be).

How to Run the Smarter Race

One of the most common challenges banks are currently facing is competition – and the subsequent need to power risk decisions faster in order to keep up. But the key is to do this without sacrificing your risk strategy. It is possible to become more agile and self-sufficient, which allows you to make faster, more accurate risk decisions and launch new products in less than half the time – and one of the best ways to do this is upgrading to next-generation decisioning technology. Look for a partner that can offer you these key elements:

Real-time data access to hundreds of data sources through a single API

  • Advanced analytics based on your unique risk profiles
  • Integrated case management for a complete end-to-end perspective on credit applications
  • The ability to handle evolving compliance regulations and security demands
  • Low-code, business-user-friendly UI that enables self-sufficiency when changing processes and iterating workflows
  • Experience with swapping out legacy technology/competitive decisioning platforms to ensure a seamless transition
Leveraging automated, integrated data and more agile risk decisioning technology can help you increase your flexibility, accuracy, and speed. With the right tools on hand, you can keep up with new entrants in the market and also meet regulatory compliance requirements, all while making more informed credit decisions that improve the customer experience – and do it faster than the competition. Because in the race for customers… speed is everything.
Ready to improve your agility and run the smarter race?
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Digital Loan Origination in Banking: Competing with Challenger Banks

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Digital Loan Origination in Banking:
Competing with Challenger Banks

The financial industry has seen a dramatic shift in recent years with the rise of challenger banks. These digital-first establishments have emerged as serious competition to traditional banks, offering more personalized and innovative services that resonate with consumers. To compete with these new players, traditional banks must improve their digital capabilities and offer more streamlined services that provide customers with a better experience.

One area where banks can focus their efforts is digital loan origination. By automating this process and integrating it into their digital platforms, banks can provide customers with faster, more efficient loan processing. This is a crucial component in building a more competitive and innovative financial institution.

Digital loan origination allows banks to gather customer information and evaluate creditworthiness quickly and accurately. By leveraging data analytics and machine learning, banks can make better lending decisions while reducing the risk of defaults. This technology also makes it possible to offer more personalized loan products, which can increase customer satisfaction and loyalty.

Traditional banks can compete by improving their digital capabilities, and digital loan origination is a key area where they can focus their efforts. By automating loan processing and leveraging data analytics and machine learning, banks can make better lending decisions and provide customers with a better experience.

The End of the Level Playing Field

After the 2009 financial crisis, trust in traditional financial institutions took a hard hit with up to 80-90% of the public viewing them as untrustworthy, according to past studies. This led to an opportunity for challenger banks to enter the market with a clean slate and build their brand without the negative sentiment experienced by traditional banks.

Challenger banks also had a technological advantage over their established counterparts. Without the burden of legacy IT systems, challenger banks were able to adopt modern technology and offer digital services with greater efficiency and agility. As a result, challenger banks are quickly gaining ground, and the traditional banks are being forced to adapt or risk being left behind.

Challenger Banks: Reshaping the Future of Banking?

As the banking industry undergoes a transformation, many experts suggest that Challenger Banks will play a significant role in shaping the future of banking and money, despite the challenges that come with innovation. Unlike traditional banks, Challenger Banks tend to embrace a start-up mentality, leveraging a minimum viable product (MVP) approach to continually refine their product portfolio until they achieve the optimal balance.

While larger banks may struggle with operating in product silos and stretching their resources too thinly, Challenger Banks can prioritize quality and customer experience, giving them a competitive edge. But how can traditional banks compete with these innovative newcomers who are leveraging cutting-edge technology and a hyper-focus on innovative products and services?

Also, read: What is Banking as a Service?

Building Consumer Trust in Banking

Traditional financial institutions may have struggled with their reputations post financial crises, but a 2019 survey by Accenture showed extremely positive results for banks when it came to customer trust:

  • An average of 77.75% of consumers (across all persona groups) trust banks to care for their long-term financial wellbeing

Results were not so strong for non-traditional financial institutions:

  • Only 35.5% of consumers (across all persona groups) trust non-traditional institutions to care for their long-term financial wellbeing

So, while banks may be lagging behind when it comes to technology, they still outperform fintechs and challenger banks when it comes to consumer trust. Financial institutions trying to compete with their challenger competition should bank on the inherent trust that consumers still hold for brick and mortar institutions as a foundation to secure long-term loyalty with customers. Is this an obvious point to make?

Absolutely. But it’s how this trust can be used to build stronger bonds and expand product offerings that offers a huge opportunity for traditional financial institutions.

Dealing with Data: Customer Trust Expands Opportunities

In a time when data breaches are common, billions of records were stolen in 2018 alone, consumers are on high alert when it comes to sharing their information.

So perhaps one of the most fascinating results of Accenture’s study is that customer trust in traditional financial institutions extends to trusting banks to keep their data secure. 80% of consumers surveyed trusted their banks enough to share additional data to receive more relevant offers.

This gives banks an incredible opportunity to create truly personalized services using data gleaned directly from customers. But banks can go further, with many consumers sticking with the same financial institution for many years, banks have been gathering an immense amount of data on customers that can be used to personalize and pre-approve offers for individuals.

Wouldn’t it be nice if your customer’s felt like you truly understood their needs by offering the right products at the right times?

As a bank there’s a lot that can be learned from how challenger banks have approached disrupting the industry. Let’s consider a standard financial category that you may offer, and how the use of technology and data can improve that experience for your customers.

Mobile Loan Origination

Customers have an increasingly strong preference for the loan origination process to be mobile-friendly and fast.

  • Accenture found that on average 81% of consumers would share more information to get faster services and approvals

Challenger banks have greatly improved the loan origination process for consumers. They’ve removed the once long, paper-filled process and made approvals almost instant – all the while accepting nothing less than improved compliance and mitigated risk.

The smart pairing of data access and automation powers much of this process. And, while the idea of a loan being commenced and approved during an afternoon at work would be laughable 20-30 years ago, now it’s expected.

Offering this type of capability can seem daunting for both a startup with 25 employees and traditional banks, but launching a mobile or web app that can collect your customer’s application details, integrates with your systems and third-party data sources, decisions that loan, and provides an approval instantly is only a matter of starting with the right technology.

Building Data into Your Loan Origination Process: Using Data to Level the Playing Field

A common challenge banks face is being able to access, orchestrate, and use data. To get the most out of their historical data and gain access to new data, banks need to find a way to draw their data into one location as a foundation for decisioning and customer personalization.

Connecting disparate systems and data silos can provide banks with a huge advantage over their competitors as they’re able to gain much deeper insights into their customers and more easily assess associated risk. But legacy technology makes this almost impossible in many organizations.

To solve these issues, banks need to look for a solution that allows them to create a decisioning ecosystem. Technology that connects the dots between their CRM, historical data, new customer data, and their loan origination processes.

It’s only by using data to predict customer needs, pre-approve products, and personalize offerings that banks will compete with the challenger banks nipping at their heels. And, if banks can match this personalization across both physical and digital channels, banks could well disrupt the disrupters!

“Our entire approach is built on simplifying banking. One of the ways we do this is by making the customer experience fast and effortless; from the initial on-boarding process through to every subsequent interaction. The Provenir Platform gives us speed and flexibility in our lending operations, which enables a customer to apply for a loan at lunchtime, receive immediate approval, and have the money available in their account later that day.”

– CEO, Instabank

How Challenger Banks Are Capturing Customers’ Hearts (And Wallets)

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10 Fintechs Accelerating SME Lending

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10 Fintechs Accelerating SME Lending

Championing SME Survival and Growth

A new wave of fintechs and neobanks has been sweeping the world of SME Lending off its feet by embracing digital technology, data, and advanced analytics like machine learning and AI. And there’s never been a better time for it. The landscape has changed dramatically for SMEs, not necessarily for the better. The potential of a global recession has consistently lowered margins and hurt SME scaling and expansion efforts. According to a recent report by the World Economic Forum, nearly two-thirds of small to mid-sized businesses (SMBs) said survival and expansion are their primary challenge.

Unlike consumer payments, the B2B variety remain mired in legacy systems and manual practices. And unlike larger, established companies who have long and secure relations with their financial institutions, SMEs have a larger need of help in accessing working capital, which remains their critical pain point.

The result? As access to working capital from traditional lenders dries up, SMEs are increasingly looking to digital-first and alternative channels. A surprising 75% of SMEs report being more likely to use a digital-only bank as their primary provider of working capital. We revisit our list of SME lending innovators, as they go from trend setters to “the new digital normal” in SME financing.

  1. OakNorth – UK-based fintech OakNorth delivers instant credit analysis and real-time portfolio insights focused on transforming commercial lending. The co-founders of OakNorth were rejected for the credit needed to grow their business numerous times, prompting them to create their Credit Intelligence platform. Their goal was to build a robust, sustainable bank but also to create software that would enable other banks to lend to SMEs that were previously underserved.
  2. NeoGrowth – Founded in 2011, India-based NeoGrowth Credit is a tech-enabled business that offers unsecured loans to small retailers in India. Combining traditional and alternate data for more accurate credit scoring, NeoGrowth also offers dynamic repayment terms and automated collections processes to help identify the most creditworthy customers. Calling themselves pioneers in SME lending based on the underwriting of digital payments data, their mission is to help small business owners drive growth that matches their ambitions. Also read: What is credit underwriting?
  3. Kabbage – Selected for the 2019 Forbes FinTech 50 startups list, Kabbage (now owned by American Express) provides SMBs with credit by evaluating business-focused alternative data like accounting info, online sales and shipping. With this more nuanced view of data to better understand performance, Kabbage is able to offer flexible credit options in real time.
  4. Banco Pichincha – In 2016, Banco Pichincha received a credit line of $55 million from the International Finance Corporation (IFC) to finance loans to women-owned SMEs in an effort to fuel the growth of female Ecuadorian entrepreneurs. Ecuador’s largest bank, they doubled down on their mission in 2019 when they signed an alliance with the Overseas Private Investment Corporation (OPIC) and Wells Fargo for a combined loan of $108 million to support loans to MSMEs in the region that are owned, led by or support women.
  5. Allica Bank – Claiming that SMEs have often been left behind by the ‘big banks,’ Allica Bank combines modern technology with local relationships to ensure SMEs have the tools and the funding they need to operate. Based in the UK, Allica Bank offers SMEs asset financing, with up to £1 million worth of flexible financing options.
  6. Judo Bank – Australia’s only challenger bank built specifically for lending to SMEs, this innovative organization seeks to bring back the lost art of relationships in business banking. Created by experienced business banking professionals, they brand themselves as a ‘genuine alternative’ for SMEs who want quick access to not only funds, but the superior customer experience they deserve.
  7. First Circle – Based in the Philippines, First Circle’s mission is to enable SMEs to achieve their full potential through fast and flexible financial partnership. Their customers often have no credit data or fixed collateral and as a result are excluded from the traditional banking sector (and therefore often forced to work with predatory lenders). First Circle allows these SMEs to secure funding in as little as a day through an automated, digitized application process.
  8. Lulalend – Sixty percent of South African businesses find it difficult to access the capital necessary to grow their business, due to long wait times, painful paperwork requirements and the necessity of high collateral. Lulalend uses AI to score creditworthiness instantly, ensuring small business owners are able to receive funding within 24 hours of applying. To date, they’ve processed over 70,000 applications and secured funding for thousands of small businesses across South Africa.
  9. Siembro – Argentinian organization Siembro uses AI to power their in-house loan algorithm, providing them the ability to offer instant loan approvals for small businesses in the area of agricultural and machinery. With over 1.5 million small and medium farm businesses in the country who have limited access to credit (and limited cash flows), Siembro focuses on ensuring corn, wheat and soy farmers obtain the funding they need to survive.
  10. Iwoca – A start-up that began when its founders noticed that small businesses were getting shut out of access to much-needed credit, iwoca is now one of the fastest-growing business lenders in Europe. Working towards a goal of funding one million small businesses, iwoca wants to ensure that SMEs have more time to run and grow their business instead of being forced to fill out endless paperwork and wait for approvals. Recently, their B2B financing solution iwocaPay integrated with Quickbooks to help small businesses with their cash flow, increasing businesses’ customer base and revenue.

Faster Loan Approvals

By embracing the use of digital technology, data, and advanced analytics like machine learning and AI, these companies have been able to simplify, and in many cases completely transform application processes. They are able to automate credit decisioning to provide accurate, real-time approvals, allowing SMEs to gain access to funds quicker than ever before. By automating data collection, risk decisioning and pricing, lenders can automate approvals and ensure funding is in hand within a matter of only days – or even hours!

The capabilities these lenders are offering are not just a critical lifeline. Their products tend to be more flexible and more personalized to each SMEs unique needs, allowing them to go from mere survival, to full-blown adaptation to a changing, uncertain environment. That is the unique power of AI-fueled, data-led tech innovation.

Also, read: What is Banking as a Service (BaaS)?

Want to find out more about how to increase SME loan approvals without increasing your risks?

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APACs Top Fintech Trends to Watch

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APAC’s Top Fintech Trends to Watch

Asia Pacific (APAC) is home to diverse markets with different levels of maturation. But whether the market is emerging or mature, fintech innovation is booming across the region. Fintechs had their strongest year yet in 2022, with a record-breaking $50.5 billion invested into the industry – this level of investment is propelling APAC’s continued growth even when other regions are seeing slowdowns.

So what are the ideas driving this growth? Where is disruption happening now and where can we expect to see it develop as technology progresses? Provenir’s Bharath Vellore shares his insights on APAC’s hottest trends to watch for Indonesia, Malaysia, Singapore, the Philippines, and Australia.

Indonesia: Buy Now, Pay Later (BNPL)

Despite the recent negative press around BNPL, there’s good news for the industry in Indonesia, where it grew by 70% to reach almost $4.5 billion in 2022. The outlook for medium to long-term growth remains very strong, with projected growth of 32.5% to reach an expected market size of $25 billion by 2028.

Why has BNPL had such success in Indonesia? It has helped the country to fill a significant lending gap. Nearly 65% of the population is unbanked and credit card penetration is in the low single digits – the need for financially inclusive credit is broad. And the ways BNPL is being used are broad as well. Similar to usage around the world, the payment option is now breaking up the lowest value grocery runs and other everyday transactions to expensive luxury retail purchases.

Some fintechs pushing forward Indonesian BNPL include:

Malaysia: Digital Banking

In 2022, Malaysia’s Central Bank awarded 5 digital banking licenses for the first time, with the intent to drive financial inclusion in the country. With digital banks now in play, consumers can access convenient and flexible financial products. A dynamic space to watch will be how these digital banking entrants will grow, given the position of the traditional lenders and banks that have been entrenched in the space for a significant period of time with large customer bases.

Provenir partner Credolab agrees, also pointing out the importance of fraud mitigation:

“A digital banking transformation is accelerating in Malaysia, amid stiff competition from other countries in the region. To manage the associated fraud risks, banks offering digital services will have to take appropriate measures and collaborate with best-of-breed Fintechs to help fight fraud.”

Steve Thurley, Managing Director – APAC, Credolab

We believe that the digital banks that find success will create a path to profitable growth by finding low cost customer acquisition models and delivering new products to market rapidly. The best way to do this is find customers through partnerships and networks, and develop financial products on a low-code/no-code platform that allows business users to be agile and responsive to market needs. The fintech difference? These products should be highly personalized and feature-rich to offer consumers elevated digital banking experiences they can’t get from traditional banks.

The financial groups launching banks are:

Singapore: Embedded Finance
Unlike Indonesia, Singapore has a very mature financial ecosystem. Banks are quite well entrenched in the economy and have even proactively adopted digital services, making room for digital banks, embedded finance, and hyper-personalized financial products. Adopting embedded finance helps organizations that aren’t traditionally financial service providers to provide financial products, reaching new market segments and simplifying the customer experience.

The biggest opportunities for innovation in embedded finance include instant payments, cross-border transactions, and micro lending. Embedded finance products for SMEs are also gaining traction, helping small businesses with accounting and managing ledgers, while providing working capital loans. Micro credit loans, such as retail financing for e-commerce, merchant loan offers based on sales volumes, and embedded payment options in apps are streamlining financial products into everyday processes and changing the way consumers are engaging with money.

These fintechs are embedding themselves as top embedded finance providers in Singapore:

The Philippines: SME Lending

Micro, small, and medium-sized businesses are the lifeblood of the Philippine economy. Almost 36% of the GDP is generated by the SME sector and 63% of workers in the country work at one. Despite the enormous presence in the country, SMEs remain largely underfinanced, which limits their – and the economy’s – ability to grow. Enter: fintechs.

As digital loans are becoming a more viable and attractive option, fintechs are extending credit to SMEs through online platforms that small business owners can access from anywhere in the country. As big data becomes more available, SME lenders are able to tap into that ecosystem to build alternative credit scoring models. There is not great coverage from the bureau point of view, as the majority of SMEs have thin files or no credit report at all, so the lack of financial data is a huge gap for traditional lenders who don’t have enough information to make accurate decisions. Big data is providing access to alternative data such as customer reviews, income flows, and more to make lending decisions – this area is primed for significant growth.

Companies driving SME lending innovation include:

Australia: Open Banking
Consumer Data Right (CDR) legislation was introduced in Australia in 2020. Phase one mandated the country’s four biggest banks to share access to consumer data; phase two did the same for small banks; last year’s phase extended to energy and utility companies; and next year’s final phase brings non-bank lenders under CDR. What happens when you’re combining datasets across banking, energy, and nonbanking? Consumers access lending products across the ecosystem and are able to take advantage of the best deals on financial products.

Provenir partner SEON highlights the importance of payment speed as well:

“Open banking allows innovation in multiple areas, including payments, credit checks, loan applications, and more. The most exciting is open banking payment initiation, which provides instant access to cash flow on a faster payment rail (funds sent and received in 2-10s) at a fraction of the cost of credit cards.”

Daniel Sebes, Strategic Director, SEON

Currently, Australia has 115 data holders of consumer data and 24 active data recipients who can receive consumer data. The number of data recipients will grow tremendously, catalyzing fintechs to build innovative financial products that push one another ahead through competition while empowering consumers to find the best products available. For this reason, CDR and open banking will be a very interesting space to keep an eye on.

Active data recipients in Australia include:

It’s clear that fintechs have disrupted almost every aspect of financial services across the APAC region. Many of these trends will continue to inspire new ways to disrupt the way we manage and access credit, whether it’s through new ways to pay for goods, the data that paints financial health, or how the small businesses driving economic growth stay afloat. Whether the trends have staying power or will evolve as technology and regulation develops, only time will tell. What we do know is we’ll be watching.

Looking for a technology partner to help you jump on one of these trends?

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Back to the Future: 8 Features of Fast and Future-Proof BNPL Technology

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Ten Fintechs Shaking Up Consumer Lending

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Ten Fintechs Shaking Up Consumer Lending

With the ever-evolving landscape of financial technology, consumer lending has never been more accessible and efficient – in large part, due to fintech innovation. With a global consumer credit market size of $11 billion, rapidly growing middle classes in emerging markets, and economic uncertainty affecting us all, the opportunity for lenders to tap into the consumer need for credit is immense.

Across the broad spectrum of consumer lending, fintechs are answering the call and disrupting the traditional. No credit score? No problem. Worried about missing payments? You’re covered. From a company supporting gig workers around the world to a credit card for foodies, these ten fintechs are shaking up auto lending, BNPL, credit cards, mortgages, and retail/POS.

Auto Lending
Lendbuzz – USA If you’re new to credit, it can be difficult to get approved for auto financing. Lendbuzz is here to change that. The fintech proves a simple and fast application process that assesses creditworthiness with data beyond just your credit score. Working directly with auto dealerships, Lendbuzz offers personalized loans and instant decisions, taking you through the process from start to finish.
Moove – EMEA and India Founded in Nigeria in 2020, Moove is a global startup that aims to democratize access to vehicle ownership for “mobility entrepreneurs” across Africa, the Middle East, Europe, and India. Tackling the high barrier to vehicle financing that millions face, especially in emerging markets, Moove uses a revenue-based financing model to offer car loans that drivers then pay off through their ridesharing app.
Buy Now, Pay Later
ShopBack (formerly Hoolah) – Southeast Asia and Australia

Singapore-born ShopBack is a fintech that provides improved shopping experiences to consumers and broader reach and shopper engagement to brands and retailers. Operating across APAC, their integrated BNPL service allows you to pay off purchases in installments of three, which can be combined with features such as cashback and prepaid retail vouchers. ShopBack hopes to make shopping “more rewarding, delightful, and accessible.”

Nelo – Mexico If you want to buy now, pay later at Mexico’s top merchants, you want to download Nelo’s top-rated app – it’s the first of its kind in the region, enabling shoppers to pay in installments with a virtual card generated at checkout. And through the company’s partnership with Mastercard, you can use it at any online merchant. You can also use it to finance everyday expenses like utilities and other bills, a mark of BNPL innovation and a sign of how the segment is likely to evolve.
Credit Cards
Cred.ai – USA Cred.ai is an AI-powered credit card designed to help users build credit while mitigating missed payments. The fintech sets up automated spending limits, helping you spend within your means, and their proprietary underwriting model means you don’t need a FICO score to apply. The card itself is metal, unicorn-themed, and free for approved applicants. It works best with their digital banking product and comes with features like an early paycheck (called flux capacitor) and digital “self-destruct” cards called stealthcards.
Yonder – London A rewards credit card “great for expats and immigrants,” Yonder is a rewards credit card that boasts no foreign exchange fees, worldwide travel insurance, and you can apply without a UK credit score. Leveraging open banking technology, the credit card is able to focus on financial inclusion while rewarding users for the experiences that enrich their lives, whether it’s travel or dining at Yonder’s curated restaurant partners around London.
Mortgage
Hypofriend – Germany Hypofriend was founded to simplify and personalize the process of getting a mortgage for Germans. They use advanced technology to analyze your optimal finance strategy while predicting bank decisions in order to connect you to a personalized mortgage offer from a lender that fits your needs. The Hypofriend team is also there to advise from start to finish, demystifying the complex process and providing transparency to support more financial literacy and understanding.
HomeCrowd – Malaysia Focused on helping Millennials in Malaysia achieve the dream of owning a home, HomeCrowd uses holistic, data-driven credit scoring to match mortgage applicants with peer-to-peer (P2P) lenders on a blockchain-powered, Web3 platform. The company is the first in the country to be licensed and regulated for P2P lending specifically for mortgages and consumer financing by the government.
Retail/Point-of-Sale (POS)
Blink – Egypt Did you know that less than 4% of Egyptians have access to credit cards? The majority of Egyptians must rely on savings or finance purchases with high-interest loans. Blnk is here to change that – they enable any consumer to receive instant credit at the point-of-sale. Their current network of merchants includes over 300 businesses and the fintech has already disbursed over $20 million in loans.
Acima – USA US-based Acima offers consumers lease-to-own solutions as an alternative to traditional retail financing. You don’t need credit to apply and your credit score isn’t affected. Simply lease the furniture, electronics, or any other item you want to purchase and “rent” it until the cost of the item is covered, or pay early at a discounted rate. If you no longer want the item, just return it! Acima enables online and in-store shopping and offers flexible payment terms.

Unlocking Consumer Lending Innovation

As access to consumer credit increases around the world, both fintechs and traditional financial service providers will need to leverage the right technology to provide it. The ten fintechs you just read about have found their innovative idea to disrupt consumer lending – what will yours be?

No matter the idea or use case, you need a technology partner that thinks like you. Future-proof your consumer lending strategy and launch new products with a data and decisioning ecosystem that manages risk, so you can focus on what matters most: serving your customers in new, disruptive ways.

Read the eBook, The Secret to Consumer Lending Sucess to discover how you can overcome any lending challenge with a robust credit risk decisioning platform that grants access to both alternative and traditional data sources through a single API.

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The Next-Generation Collections Model

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The Next-Generation
Collections Model

Enabled by Advanced Analytics

The economic environment is changing, and your organization needs to adapt to remain competitive. Financial institutions, energy, telcom, auto, utilities, and retail finance companies have each recognized the need to build a new collections model that utilizes advanced analytics and outcomes to drive processes, rather than simply relying on static info like days past due.

Unfortunately, the collections industry has been relatively slow to embrace new techniques in analytics compared to other areas of organizations (i.e. like loan origination) yet nearly 30% of Americans have at least one debt currently in collections. Investment in the collections process is often overlooked in favor of projects that aim to grow the customer base. However, with consumer debt levels returning to 2008 recession levels (total household debt in the United States rose by $148 billion in Q1 2023, totalling $17.05 trillion) and the threat of another recession on the horizon, collections centers are finally getting the attention they deserve. In this blog, we’ll look at the new technologies available, how they impact the process, and ways to utilize new tech to stay ahead of your competition.

The New Collections Model
Regulatory concerns, consumer preferences, and increasing consumer debt levels have all created a need to revisit and renew the collections process. In expanding credit markets, new technologies have already been embraced to enhance the customer experience in the credit acquisition process. But now it’s time to apply the same approach elsewhere.

The new collections model needs to focus on analytics and new technologies, which were unavailable during the last downturn. If you’re a risk manager, it’s important to ensure that your organization is prepared to manage economic uncertainty. Embracing advanced analytics and outcomes-driven processes can help your organization stay ahead of the curve and maintain a competitive edge. Implement a new model that is optimized for success – and ensure your organization won’t fall behind.

Advanced Analytics and Technology for Next-Gen Collections
The collections industry has been slow to embrace analytical methods. But advancements in analytical methods and machine learning, coupled with digital technologies, have created new opportunities, enabling more effective and efficient collections processes, and revolutionizing the way lenders interact with customers. Utilizing these advanced analytics means financial institutions, energy, telcom, utility companies, and retail finance companies can build a more efficient model, resulting in better performance at a lower cost.

Customer segmentation can also be improved, capturing a more holistic view of the delinquent customer. This includes their ability and willingness to pay, intent to pay, and contact channel preference. Driven by analytics, this new approach determines the best possible treatment strategy, the ideal way to communicate, and the optimal moment to make contact. By matching the most appropriate forbearance strategy for each customer and communicating via their preferred channel, financial institutions can optimize both the customer experience and the cost to collect.

For the past 30 years, traditional collections processes have heavily relied on behavior scoring, days past due, and balance to prioritize outbound call strategies. However, this approach is no longer sufficient in today’s market. Advanced analytics can enable the development of more effective collection strategies by providing finer segmentation and a wider variety of customer contact possibilities. This creates a more diverse suite of channels for customer communication, which improves customer experience and provides a greater degree of control in lender-customer interactions. This shift marks a dramatic change from the traditional collections process, which relies on static classifications like days past due or risk scores to drive decision-making. By adopting a more dynamic approach that focuses on outcomes and response propensity, lenders can provide more individualized treatments that better reflect customer preferences and circumstances.

Above all else, using advanced analytics and tech advancements like artificial intelligence and machine learning enables financial institutions to migrate to a deeper, more informed treatment of their at-risk customers. By learning from previous collections activities, the assignment of treatments becomes more fine-tuned and effective over time, generating considerable efficiencies while enhancing the overall customer experience.

What data elements are required?
Overall, a combination of on-us behavioral data, off-us behavioral data, previous contact history data, and socio-demographic data is required to build a comprehensive and holistic view of the delinquent customer.
  • On-us behavioral data includes the customer’s payment history, delinquency history, and returned checks, among other attributes.
  • Off-us behavioral data involves third-party data sources that provide insights into a customer’s financial obligations and commitments, as well as updates on their behavior based on almost real-time updates.
  • Previous contact history data is critical in learning from previous contact attempts and modifying the treatment approach accordingly.
  • Socio-demographic data can be used to build customer profiles to assist in selecting the appropriate channel of communication.
Leveraging these various data sources and applying advanced analytics, allows you to build a more individualized approach to collections, based on customer preferences and circumstances. This new approach marks a significant departure from the current model, which relies on core static classifications such as days past due or single risk scores. With the next-generation collections model, the final customer treatment is much more personalized, focused on outcomes and response propensity.
The Role of the Decision Engine
It may seem daunting to implement more advanced technologies in your collections strategy, but the role of an automated decision engine is key. Using real-time data and and automated risk decisioning is the background superstar that enhances your collections process in a variety of ways:
  • Prioritization of Debtors: Use machine learning algorithms to analyze payment history, financial status and other data to immediately predict likelihood of default or late payment and allows you to prioritize collection efforts to improve efficiency and effectiveness.
  • Personalized Collection Strategies: As mentioned above, tailored treatment strategies mean more effective outcomes and higher recovery rates.
  • Real-Time Decision Making: Making decisions in real-time allows you to move quickly and adjust collection strategies as new data becomes available.
  • Reduced Operational Costs: Limit the need for manual work and enable 24/7 operations without additional staffing costs, thanks to automation of decisions, real-time data integration, and machine learning optimizations.
  • Improved Compliance: Automated risk decisioning processes, for collections or otherwise, can be programmed to follow relevant regulations and policies (allowing for regional differences too), and reduces the risk of non-compliance.
  • Enhanced Customer Experience: No one enjoys the collections process, but as previously discussed, the more personal, respectful, and appropriate the treatment strategy, the more easily you can preserve the customer relationship.
Traditional collections processes heavily relied on simplistic measures like behavior scoring, days past due, and balance to prioritize outbound call strategies. But in today’s dynamic, rapidly changing market, this approach falls short. As the industry continues to evolve, it’s imperative for collections professionals to recognize the transformative potential of analytics and leverage them to create a competitive advantage in the dynamic collections landscape. To do so may require a new look at the decisioning platform used in collections – because if you aren’t adapting to the conditions, your competitors will.

Also, read:

What is credit underwriting?

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Ten Fintechs/Finservs Supporting Women – or Being Led by Them!

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Ten Fintechs/Finservs Supporting Women – 
or Being Led by Them!

Celebrating International Women’s Day in Fintech – #EmbraceEquity

Wednesday March 8, 2023 is International Women’s Day – a day earmarked to celebrate the achievements of women globally, and draw attention to the persistent lack of equality around the world. Everyone wins when gender bias, stereotypes and discrimination are minimized, but it’s easy to pay lip-service to these sorts of holidays and much harder to actually do anything about it. The theme of this year’s International Women’s Day is #EmbraceEquity – looking at how collectively we can strive forward towards a more diverse, equitable, inclusive world.

Women frequently remain underserved by traditional financial services institutions, or unfairly scored when it comes to credit products. Even as recently as the 1960s in North America, unmarried women couldn’t get access to credit or a bank account (and married women needed their husband’s permission). Despite the strides the world has taken in gender equality, there’s still a large divide in terms of financial inclusion – with some reports claiming that the “gender gap remains unaltered since 2011.”

“In seeking mortgages, women are charged higher rates and denied more often, despite being more likely to repay their loans than men with the same FICO score, loan-to-value, and income. This means that for women, offering the same treatment for the same credit profile as a man is wrong, because the woman will actually default less. The issue is exacerbated by the fact that income is a key factor in mortgage rates, and women earn just $0.84 for every $1.00 earned by men.”

What impact does this economic divide really have? Research shows that eliminating the gender gap in financial inclusion would have continued positive effects on the economy – increasing its overall size, boosting consumption rates, lowering financial risks and facilitating new business opportunities. Closing the gap can help enable a nation’s overall “development, economic growth, inequality reduction, business evolution and social inclusion.”

How Can Technology Help Close the Gap?

There are numerous ways that fintechs and their use of cutting-edge technology (like machine learning, artificial intelligence, and alternative data) can be a catalyst for change – enabling a more even playing field for women and other underserved populations. The use of alternative data can supplement traditional credit scoring methods, ensuring inclusion for women who lack credit histories. AI and machine learning can integrate that alternative data more easily, deploy advanced models to manage bias and improve risk decisioning accuracy – encouraging financial inclusion as a result and helping ensure a more equitable financial services landscape.

There’s still lots of work to be done and using this sort of technology requires intentionality and partnership with financial services providers and organizations that help ensure gender equality. But how can fintechs work to #EmbraceEquity when so few of them have women in leadership positions? Only 12% of fintech founders or co-founders globally are women, and only 6% of fintechs have female CEOs. A startling lack of female representation in the fintech industry has a direct impact on the types of products and services the industry offers its consumers (of course, half of which could potentially be women). And to put it in terms of dollars and cents – “the lack of gender diversity in the industry decreases the organizational and financial performance of businesses.”

To further the cause of International Women’s Day and to help #EmbraceEquity, we’re highlighting ten innovative organizations that are women-led fintechs or are using the power of fintech to ensure financial inclusion – and helping improve the lives of women and the economy along the way.

  • Tala: A global fintech with a mission to create the ‘world’s most accessible financial services,’ Tala aims to help underbanked consumers borrow, save, and grow their money. With a modern credit infrastructure built in-house, the company uses advanced data science and machine learning to enable instant credit decisions for their consumers. Shivani Siroya is the female Founder & CEO of Tala, and the company boasts two more female C-Suite executives, Kelly Uphoff as CTO and Jen Loo as CFO.
  • Jefa: A challenger bank based in Latin America, this organization focuses on women without a traditional bank account, and aims to help them solve the problems faced when trying to open/manage an account. The all-digital bank targets women in emerging countries who may not have access to traditional banks (even physical access, like transportation to get to a branch), and requires no minimum balance. Future developments include a network of inclusive merchants and a credit building platform.
  • Sequin: While traditional debit cards don’t contribute to credit building, the Sequin card does. Aimed specifically at women, the card helps you build credit with each purchase, without requiring credit checks or imposing late fees. Highlighting the systemic bias sometimes reflected in traditional credit scoring algorithms, the Sequin card helps correct this by not reporting credit utilization to credit bureaus.
  • Kaleidofin: This India-based payment platform offers ‘doorstep service’ aimed at women, helping them build personal financial management plans and offering discretion and privacy to ensure safety for customers. For example, customers can check their balance via ‘missed calls’ and set up a proxy outside their household to receive messages about their accounts.
  • Pezesha: Founded by a woman and marketed at SMEs and individuals in Kenya, Pezesha focuses directly on informal savings groups and designs incentives around them, offering a credit-score-as-a-service product and financial education. Since its founding, more than 50% of women in the region have been included in their financial ecosystem.
  • Ellevest: Founded by Sallie Krawcheck, the former head of Bank of America’s Global Wealth and Investment Management division, U.S.-based investment firm Ellevest markets itself as a tool built by women, for women. The company’s proprietary investment algorithm and tailored advice considers specific women-focused issues, including career breaks for maternity leave or caregiving, longer average lifespans, unpaid female labor and pay gaps.
  • Oraan: To help combat the fact that 41% of women in Pakistan save money through informal groups/committees, Oraan (Pakistan’s first women-led fintech startup) offers financial products that provide women the opportunity to save and borrow money from outside of their immediate social and geographical networks. Using technology, data and a ‘human-centric’ design methodology to digitize financial offerings, the company aims to make saving money both simple and safe for women.
  • HerVest: This Nigerian investment firms aims to bring financial inclusion and empowerment to more African women, helping to bridge the economic gender gap and improving lives with greater access to financial services. With a specific focus in agriculture, HerVest provides female farmers growth opportunities relating to crops, grain banking and livestock.
  • Starling Bank – A digital challenger bank that remains one of the UKs fastest growing banks, Starling Bank has also been named Britian’s best four years in a row. CEO Anne Boden founded the company in 2014 at the age of 54 – and despite challenges and setbacks the bank has flourished under her leadership. In late 2020 Anne released a memoir outlining her struggles as a 50+ female trying to break down barriers in the male-dominated fintech world.
  • Borrowell – A Canadian fintech success story, Borrowell was the first in Canada to offer free access to credit scores and uses an AI-powered credit coach to help customers achieve their financial goals. Female Co-Founder and COO Eva Wong is an outspoken advocate for diversity and inclusion – and the organization’s commitment to the cause has it listed as one of the Best Workplaces for Women by Great Places to Work Canada.

While there is still plenty of work to be done to ensure equity for all genders in financial services, it’s refreshing to see so many innovative fintechs discovering new and unique ways to empower women and encourage inclusivity and diversity. And the more we choose to represent women in leadership/executive roles, the better!

Discover how simplified access to a variety of data sources (including alternative data) can help you embrace equity in your risk decisions.

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Payday Loan vs. Unarranged Overdraft: Which is More Expensive?

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Payday Loan vs. Unarranged Overdraft:
Which is More Expensive?

When it comes to borrowing money, people often think of payday loans as a costly option due to high-interest rates and fees. However, according to consumer group Which?, dipping into an unarranged overdraft can be more expensive than a payday loan.

In this no-nonsense guide, we will explore the differences between payday loans and unarranged overdrafts, including the pros and cons of each option.

Payday Loans

A payday loan is a short-term loan typically used to cover unexpected expenses or emergencies. Here are some key points to keep in mind:

Pros:

  • Quick access to cash
  • Easy to apply for
  • Fixed fees

Cons:

  • High-interest rates
  • Short repayment periods
  • Can lead to a cycle of debt if not managed properly

Unarranged Overdrafts

An unarranged overdraft is when you spend more money than you have in your bank account and don’t have an agreed-upon overdraft limit in place. Here are some things to consider:

Pros:

  • Quick access to cash
  • No need for pre-approval
  • Can cover unexpected expenses

Cons:

  • High fees and interest rates
  • Can lead to a cycle of debt if not managed properly
  • No cap on charges, can be more expensive than a payday loan

Comparing Costs

As mentioned earlier, the charge for a £100 payday loan over 28 days has been capped at £22.40 since January 2015. In contrast, going overdrawn on an unarranged overdraft for the same amount and period can cost up to £90.

While banks do offer loan services, including arranged overdrafts, these options may not be accessible to everyone. Many customers go overdrawn when they cannot get arranged borrowing or during a short-term cash flow situation. The Financial Conduct Authority introduced the cap on payday loans to protect these borrowers, but there is no similar cap on unarranged bank overdrafts.

Payday loans are often viewed as a costly way to borrow money, an unarranged overdraft can be even more expensive. It’s essential to understand the pros and cons of both options and ensure you are aware of all fees and charges before making a decision.

When considering borrowing money, always evaluate your options carefully, and only take out a loan or overdraft if you can afford to repay it on time. With the right approach, borrowing can be a useful tool to help manage short-term financial difficulties.

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Provenir’s Data Integration Tools

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Provenir’s Data Integration Tools

Data Integration Tools To Access the Data You Need When You Need It

As a financial services organization you know that having access to the right data at the right time is essential for smarter decisioning. But, it’s more than that. The right data will make you more competitive, more agile, and ready to rapidly respond to evolving business needs. Put data access in the hands of your business users with Provenir data integration tools!

Provenir can be quickly and easily integrated with any data source, whether internal, external, structured or unstructured. In today’s digital world new data sources are constantly emerging. When a new data source becomes available that you want to access integration can often take weeks or months, which means that you miss out on valuable information and opportunities while you wait to get connected.

Not with Provenir.

If you want to integrate to a new data source Provenir data integration tools give you the power to create integrations in a visual environment. So, no coding, no dependency on us, and no long waits to get connected. In fact, integrations can be completed in hours. Simply use our data integration tools to create the connection you need to start using the data now, not months from now.

Building Your Risk Analytics Ecosystem

As a financial services organization you’re always looking at the bigger picture. Your organization doesn’t exist in a vacuum and neither should your risk system. To fully understand risks and explore new opportunities you need technology that empowers you to build one cohesive risk analytics ecosystem that connects across all of your business systems and with essential external data sources.

We work with clients just like you who are looking for an efficient way to build their risk ecosystem and they want to know; how do we simplify integration to support a cohesive risk system?

To make the answer simple, Provenir data integration tools offer different routes to connectivity:

  • Integration Adapter—to connect with any data source, both internal and external, with ease
  • Pre-Built Adapters—to reduce integration time to Salesforce, AmazonML, and Spark ML
  • ProvAPI—to develop and expose business functions and models as discrete services

Provenir Integration Adapters

You can use the Provenir Adapter technology to create integrations with some of the most popular data sources in the financial services industry, including FICO, Dun & Bradstreet, Experian, Lexis Nexis, Moody’s, Kelley Blue Book, TransUnion, and many others.

But, as an innovative financial services company, you’re probably looking to explore alternative data sources too. The Integration Adapter can connect to any source.

Our integration capabilities offer:

  • Connectivity, security, transaction support and, data conversion, parsing, and transformation.
  • Two-way communication so you can listen, gather, evaluate, orchestrate, analyze, and respond.
  • A visual or graphical data mapper guides the user through the task of establishing the integration and mapping the required input/output data.
  • Visual testing to check the accuracy of the integration – tests can be run independently or placed in a business logic process for a more comprehensive test. (Provenir provides instant feedback along with a detailed breakdown of the results to show you exactly what happened during the test.)

In addition to our flexible cloud-based data integration platform we also offer a selection of pre-built adapters:

Provenir Integration Adapter for Salesforce

Salesforce is the go-to customer relationship management (CRM) solution for many financial services firms. By pairing Provenir with Salesforce, you can:

  • Eliminate the manual work required to move data between legacy systems with Provenir’s ability to listen for, read, and write data into and out of Salesforce.
  • Automatically decision applications, displaying results to your loan originations interface within Salesforce.
  • Leverage information aggregated from Salesforce and other systems to generate customer-specific, real-time sales and marketing offers.

Amazon Machine Learning Integration Adapter

Using this adapter, Amazon’s Machine Learning service automatically feeds the predictive score returned by the Amazon Machine Learning model into the risk decisioning process in Provenir. The Provenir Platform then automates that process, instantly executing a pass, fail or refer result from a risk score. This powerful adapter:

  • Makes machine learning models more accessible to lenders that don’t employ dedicated machine learning experts.
  • Can give you a head start on machine learning with Amazon’s as-a-service model while capturing the full value of complex risk analytics and decisioning with Provenir.

Spark ML Integration Adapter

With this adapter you can feed the score from Spark ML into the risk decisioning process in Provenir. The Provenir Platform then uses the score to automatically return a pass, fail, or refer result. The Spark ML adapter:

  • Makes it easy to expose data to a huge variety of machine learning models.
  • Lets you combine the power of advanced machine learning with Provenir’s sophisticated decisioning and data analytics capabilities.

Modernize Your Risk Stack with ProvAPI

You want to build a future proof risk analytics solution, we get it. Why waste time creating the perfect technology stack if you then have to replace it in a couple of years?

An essential component for future-proof technology is having the ability to develop and expose business functions as discrete services. That’s why Provenir is designed to support a Microservices architecture and the steps needed to move to one.

Provenir is:

  • Distributed – Can be deployed full stack or distributed by functionality.
  • Container Ready – Compatible with Amazon Container Service and Docker.
  • Extendable – Users maintain control with the power to add screens and Platform REST API’s.
  • Monitored – Cloud admins are alerted for all events occurring outside established thresholds and performance SLA’s.
  • User-friendly – Data and functionality within Provenir is exposed using the visual ProvAPI interface.
  • Scalable – Provenir supports cubernates and autoscaling so the technology can easily adapt to changing business needs.

With Provenir, you have the power to create REST APIs, which means the opportunities are endless. Using ProvAPI, you can expose the following (and much more) for use in a decisioning process.

  • Models and Scorecards
  • Age Calculations
  • Blacklist and OFAC Checks
  • Calls to third-party data providers
  • And more

Machine Learning Credit Risk Models are More Accessible Than You Thought

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