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Finally, The Secret to Credit Risk Modeling with Python

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Finally, The Secret to Credit Risk Modeling with Python

I’m going to defer to a quote from the Banker Scene in Monty Python’s Flying Circus to start this blog:

“I’m glad to say we’ve got the go-ahead to lend you the money you required…

We will, of course, want as security the deeds of your house, of your aunt’s house, of your second cousin’s house, of your wife’s parents’ house, and of your granny’s bungalow. And we will, in addition, need a controlling interest in your new company, unrestricted access to your private bank account, the deposit in our vaults of your three children as hostages…”

It may seem a little odd to quote Monty Python at the start of a blog about credit risk modeling using Python but, I have two very good reasons:

  1. While the level of security the borrower in this scene needed to provide is absurd, it highlights the need for lenders to fully understand the risk of a loan in order to not require their customers to put their children up as collateral…
  2. You’ll find out in a second

When Monty Python was filmed back in 1969 credit risk modeling was in its infancy, in fact it was barely crawling let alone walking into adolescence. Understanding default risk was a complicated, long, and labor-intensive process. We can thank risk modeling pioneers and languages like Python for helping banks and other lenders make more informed decisions about loan applications.

A Brief Recap on the History of Python

It was the week of Christmas in 1989 in Amsterdam, 48* F and cloudy, when Guido van Rossum started tinkering on a small project to busy himself while his employer’s office closed for the holiday. van Rossum set out to create a language, based on a previous project called ABC, that was reliant on the Unix infrastructure and conventions, without being Unix-bound. He placed a high emphasis on readability and uniformity in an era that praised languages like C and Perl (PHP’s high-maintenance personality would crash the party later). The result was a gorgeously elegant open source language named after (here’s reason 2) Monty Python’s Flying Circus.

Since its official release in 1993, Python has displayed its prowess across multiple industries and in widely varying use cases. It is now the most widely taught introductory language in the top computer science programs worldwide, was named the most in-demand programming language in the U.S. by Forbes’ fintech columnist, and hit #2 on the list of most GitHub pulls by language in 2017.

Python Risk Modeling in Finance

One increasingly popular application of Python is in credit risk modeling. Many brilliant data scientists and analysts wrangle the usability of Python to implement machine learning and deep learning algorithms. From simple algorithms like logistic regression, decision trees, random forests, support vector machines via classification and regression, to more advanced methods like clustering and neural networks, the many advantages of Python are opening the doors to apply artificial intelligence to credit risk challenges.

Yet, despite the promise that Python and other popular languages bring to financial services, we talk to companies nearly every day who vent frustration with the process of testing and deploying models to production, then maintaining or changing models as business objectives evolve. It has been stated that “The next breakthrough in data analysis may not be in individual algorithms, but in the ability to rapidly combine, deploy, and maintain existing algorithms.” I would venture to guess that most of us in financial services (or any data-driven industry, for that matter) would say, “Of course.” Delays in model deployment are nothing new, and we’re ready for a change.

Challenges Deploying Credit Risk Models using Python

Ben Lorica, the Chief Data Scientist at O’Reilly once called out the delays that exist from modeling to production, identifying two root causes for the long analytical lifecycle:

  1. Silos -Data Scientists and Production Engineering teams are historically divided, resulting in a wall between the two organizations that results in inherent delays.
  2. Recoding – Models often have to be recorded before they can be deployed into production. So, while your data scientist may prefer Python risk modeling, your production system probably requires Java.

You’re probably squirming in your seat right now because you get the same familiar anxiety that I do when thinking about this dynamic. But, stay with me.

It Doesn’t Have to Be This Way

If everybody is so frustrated with this problem, why doesn’t anyone fix it?

I’m so glad you asked.

Provenir tackled this challenge head on in two very distinct ways:

  1. Empowerment – Silos or no silos, your production system should be so easy to use that your data scientist can deploy a model autonomously. Deploying a model into a Provenir decisioning environment is as simple as attaching a document to an email. Just upload the document, visually map your values, and go.
  2. Native Operationalization – Recoding?! That sounds like re-work to me. Why not operationalize models in their native languages? So, Provenir supports the native operationalization of risk models in R, SaS, and Excel along with PMML and MathML. We also include support for Python, not just because we value any opportunity to share a Monty Python joke, but because we know how beneficial it is to let your team create models in the language they’re most comfortable with!

R and Python are two of the most popular programming languages in the analytical domain, learn more.

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7 Reasons to Use Salesforce for Credit/Loan Origination and Risk Decisioning

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7 Reasons to Use Salesforce
for Credit/Loan Origination and Risk Decisioning

I had breakfast in an original Paramount Diner over the weekend. This authentic throwback to the 60s included fully restored jukeboxes on each table, letting diners choose their own private dining soundtrack.

So, what do my eating habits have to do with using Salesforce for loan origination and risk decisioning?

Well, this jukebox had a long list of song options that the diner needed to scroll through until they found something they liked. But the list was long, why? Because the diner wanted to include a range of tracks so that there was something for everyone.

It struck me as I watched the 10-year old at the table scroll through a range of classic tracks that he declared were ‘rubbish’ until he reached the pages of recent pop tracks that I declared were rubbish, that this experience was very much like shopping for financial services.

You’re often forced to scroll through the irrelevant offers until you find the one you’re ready to press play on. But, it doesn’t end there. You then you have to fill in the application and then if you’re lucky only wait a few seconds for your application to be approved.

But, financial services providers have the power to improve that experience when they combine the data within their CRM systems with their loan decisioning technology.

Using Salesforce to improve efficiency, make smarter decisions, and personalize user experience

Financial institutions want to serve customers well, so they strive for efficiency improvements. Process automation plays a big part in this. Manual, disconnected credit and lending processes are being weeded out and replaced with digital, automated solutions.

This is progress. But for complete efficiency, risk analytics and decision-making should be tied into other business systems. Salesforce is an excellent case in point. Its customer relationship management (CRM) solution is widely used by financial institutions to manage customer interactions.

Many banks, card issuers, and fintech companies manually extract and duplicate data from Salesforce to complete credit checks, risk scoring and due diligence processes using legacy systems.

This is slow and inefficient. And it can change. When credit and lending decisioning processes and Salesforce are connected, there can be seamless data exchange. Through connected ecosystems, a single data set can drive real-time risk analytics and decisioning.

The right technology, pre-integrated with Salesforce can help automate loan and credit origination. It can help your business:

  1. Increase use of Salesforce CRM data throughout the organization – listening for, reading and writing data into and out of Salesforce eliminates the manual moving of data from Salesforce to legacy systems. Technology can also enrich native Salesforce data with information maintained in other systems, which can be created and stored as custom fields within Salesforce.
  2. Automate originations and underwriting processes – by leveraging decisioning technology that can easily integrate to external and internal data sources and bureaus, organizations can make real time decisions based on the aggregated data, operationalize any risk models in minutes and use Salesforce to automate originations and underwriting. Also read: What is credit underwriting?
  3. Create a more transparent lending process – with a 360-degree view of your customers at a glance. You can unify your entire lending business through a single platform, giving borrowers, lenders, brokers, underwriters, and every member of your team a transparent view of the lending process.
  4. Provide end-to-end compliance and better reporting – automatically aggregated data from internal systems, KYCnet, and other external systems can be made available to a compliance interface built within Salesforce. Capabilities such as business rules that ensure only the right data is aggregated for each client simplify compliance end-to-end.
  5. Tailor product offers to the right customers – customers expect companies to know what they need when they need it. Combining a CRM such as Salesforce with a credit decisioning system allows businesses to collate the data they need by connected siloed data. So, you can take a consumer—not product—centric approach.
  6. Preapprove offers for existing customers – in addition to providing a customer focused offer, integrating Salesforce with a loan decisioning solution allows a business to preapprove customers for specific offers. This ensures that you only promote offers that are suitable for the customer and improves the application process.
  7. Target customers based on life events, financial triggers, or specific behaviors  data analytics can help your business predict the need for financial services based on event or behavior triggers such as marriage, saving habits, or even reduced the use of their existing products. With an integrated CRM and decisioning solution you’ll be able to not just predict the need for services but also choose the right product and preapprove the customer before reaching out through a tailored marketing campaign.

The benefits of using Salesforce for Credit/Loan origination and risk decisioning

The number of benefits that combining a CRM such as Salesforce are many and they don’t just offer small opportunities to advance your lending business. In fact, this perfect combination of technologies will empower your business to create a smarter, faster, and more customer centric user experience.

It will also bring many business gains such as the automation of manual lending processes, better KYC monitoring, and smarter decisioning to reduce risk. One huge opportunity these technologies, when used together, offer is the chance to grow and evolve your business. With analytics and customer knowledge deeply ingrained into your origination technology you’ll have a much clearer understanding of consumer needs, empowering your business to better target customers and develop products that best meet the needs of an evolving market.

Using Provenir within your Salesforce environment

With the Provenir pre-built integration adapter for Salesforce, financial institutions can automate complex analytics and decisioning processes for credit and loan applications from within their Salesforce environment.

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Python Vs. R

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Python Vs. R

The 90’s were responsible for a number of incredible developments including the internet, which forever changed the world. 90’s culture isn’t often seen in a positive light, but don’t forget it was the decade that bought both Python and R into the world. These two programing languages gave data scientists an immense amount of power to operationalize risk models, and in turn created the Python vs. R debate that’s still argued 30 years later.

When it’s time to choose the right programing option for your next risk model wouldn’t it be nice if selecting a model language was as simple as Neo’s choice in the Matrix?

“You take the blue pill—the story ends, you wake up in your bed and believe whatever you want to believe. You take the red pill—you stay in Wonderland, and I show you how deep the rabbit hole goes.”

After all, when it comes to risk analysis and data analytics the answer is easy, you need the red pill to get answers…

The red option lets you jump into the data rabbit hole, analyze the information, and get the answers you need to solve your risk questions.

So, what does that mean for Python vs. R? It means the question is, “would you like this red pill or this other red pill?”

Choosing Your Medicine—Which pill will answer your risk questions?

R and Python are two of the most popular programming languages in the analytical domain and are considered close contenders by many data analysts and scientists. Take a look at what they have in common:

  • they’re free
  • they’re supported by active communities
  • they offer open source tools and libraries

As awesome as these similarities are, the fact that both R and Python tick all three boxes can often make it difficult to choose one over the other.

In the Matrix, which we’d like to point out was another stellar 90s creation, Morpheus gave Neo the pill for a specific use—to identify his body’s signal from millions of others, then use that information to collect him. It’s not unlike a risk or analytics model, where you need the right code to collect and analyze the required data. So, with both Python and R offering powerful modeling capabilities to grant you entry to the data rabbit hole, the real question is: Which red pill offers the easiest route to the data and provides the results in a useable way?

Consider how the model will be used

So, it’s not just the capabilities of a program that influence the preference of R or Python, it’s also the context it’s being used in. R’s strength is in statistical and graphical models, and it sees more adoption from academicians, data scientists, and statisticians. Whereas, Python, which focuses more on productivity and code readability, is popular with developers, engineers, and programmers.

As a general-purpose language Python is widely used in many fields including web development. It’s also gaining popularity across investment banking and hedge funds, and is deployed by banks for pricing, risk management, and trade management platforms. Yet, surprisingly, unlike R, knowing Python is not yet a common requirement for tech talent working in most areas of financial services. So, in the Python vs. R debate, data scientists with a heavy software engineering background may prefer Python, while statisticians may rely more on R.

Usability

Python has acquired a positive response from data scientists involved in machine learning. Since the learning curve is low for its users, Python’s real strength lies in its simplicity, unmatched readability, and flexibility—all powered by a precise and efficient syntax. Since it is a full-fledged programming language, Python is great for implementing algorithms for production use as well as for integrating web apps in data analytical tasks.

On the other hand, R is great for exploratory work and is suitable for complex statistical analysis, owed to its growing number of packages. But the drawback for R beginners is that R has a steep learning curve and often makes the search for packages difficult. This can prolong the data analysis process and cause delays in implementation. While R is a great tool, it is limited in terms of what it can accomplish beyond data analysis. Many of the user libraries in R are poorly written and often considered slow, which can be an issue for users.

Library and Packages

Python has extensive libraries that significantly reduce the time span between project commencement and meaningful results. The repository of software for the Python programming language is so rich that the Python Package Index (PyPI) currently comprises of 130,641 packages. The library has a variety of environments to test and compare machine learning algorithms.

The packages offer solutions that are not only intuitive but also flexible. A good example is PyBrain, which is a modular machine learning library offering powerful algorithms for machine learning tasks. Considered to be a popular machine learning library, Scikit-learn offers data-mining tools to bolster Python’s existing superior machine learning usability.

In comparison, CRAN (Comprehensive R Archive Network) remains a huge repository with 10,000 packages that can be easily installed in R. Active users contribute in the growing repository on a daily basis and many of the capabilities of R (like statistical computing, data visualization) are unmatched. While the learning curve for beginners is steep, once a user knows the basics, it becomes much quicker to learn advanced techniques. For many statisticians, implementation and documentation in R are more approachable than in Python.

But newly installed packages in both Python and R are alleviating the weaknesses that each suffers. For example, Altair for Python and dplyr for R support the traditional flow of data visualization and data wrangling.

Data Visualization

Data Visualizations is an integral part of data analysis and can simplify complex information by identifying patterns and correlations.

R’s visualization packages include ggplot2, ggvis, googleVis, and rCharts. Visualizations through R can efficiently and effectively, make the most complex raw data set look informative and pleasing to the eye.

When compared to R, Python has a huge amount of interactive options like Geoplotlib and Bokeh and picking the best and most relevant can sometimes get exhausting and complex. Data visualization is delivered better through R and appears less complicated.

Choosing Between R and Python

So far, Python is considered a challenger to R and remains more popular due to its wide-usability and because it can implement production code. But to be fair, both R and Python come with their own set of pros and cons, and the decision to deploy the right one primarily depends on what kind of data set you are looking at and what problem you need to solve.

Both are constantly developing at a rapid pace and there is currently no universal standard for picking one over the other.

How to Integrate Risk Models Without Wasting Time and Money

Whether they choose Python, R, or another option, companies spend huge amounts of time developing risk models to figure out which customers provide the least risk for their business. One of the biggest challenges businesses face is how to operationalize these models quickly and efficiently. This can be especially difficult with complex models that are made possible with R and Python as many risk ‘solutions’ require the models to be translated into code that it can understand. If your business is using one of these solutions you’ve probably already experienced the high cost and excessive time needed to connect your latest model to your risk decisioning process.

As simple solution to these pain points is to use a model-agnostic risk decisioning solution. With a model-agnostic risk platform you’re free to choose the risk model that helps you navigate through the rabbit hole and secure the risk answers that you need to keep your business moving forward. The Provenir Risk Decisioning Platform is a great example of this model-agnostic approach. By using simple wizards risk models developed in a variety of tools can easily be imported, mapped and validated. Provenir automatically generates a list of the data fields; all you need to do is pick the data Provenir needs to send to the model, as well as the data the model should send back to Provenir to drive the decisioning. This entire process takes just a few minutes, which means you not only gain an effective way to maximize the value of your models, but can also instantly adapt risk decisioning processes whenever a model changes.

If your business is wasting time and money implementing risk models take some advice from Morpheus in the Matrix, “What are you waiting for? With Provenir you’re faster than this.”

What? That’s exactly how it went in the movie…

See how simple it is to operationalize risk models in Provenir with this insider how-to.

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Merge Ahead – What Happens When Buy Now, Pay Later and the Credit Card Industry Intersect?

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Merge Ahead –
What Happens When Buy Now, Pay Later and the Credit Card Industry Intersect?

Accelerating growth by working together

The BNPL industry, while similar in theory to credit cards, has provided a unique twist on credit – it’s immediate, it’s typically for a single purchase (traditional BNPL products don’t offer an open-ended credit limit) and there is usually a set installment plan for repayments (most often three or four).

But even that is changing – as more BNPL providers enter the growing market space, they are changing the rules (and the products offered) at a rapid pace. As providers and products continue to evolve, the wants and needs of customers are coming sharply into focus – for example, some consumers have expressed frustration at the one-and-done aspect of installment plans at checkout, and would rather have a revolving, renewable credit limit, which certain providers now offer.

BNPL in its current state began as a 21st century, usually internet-based alternative to credit cards, allowing consumers to purchase items at point of sale (either online or in a physical store location) via installment plans. Australia is often credited with being the pioneer of BNPL, with giants like AfterPay and OpenPay, but Sweden-based Klarna brought the movement to Europe and other companies began offering BNPL services across the globe in quick succession. As more regulatory oversight comes into play and more widely varied BNPL products emerge on the scene, industry analysts and other providers may increasingly look to how Australian products react to shifts in trends as an indicator for how the evolution of the market will play out globally.

It’s clear the fintech buzz-term of the decade has to be Buy Now, Pay Later – it’s impossible to get away from. But as BNPL continues to grow and evolve so rapidly, where does that leave the credit card industry?

BNPL Crashes Credit Cards’ Party

The impact of the meteoric rise of BNPL has not gone unnoticed by the credit card industry. Consumers, especially the younger generation, have been actively looking for alternatives to high-interest credit cards, forcing traditional lenders to be more competitive if they want to stay relevant. Sixty-two percent of current BNPL customers think that the payment concept could completely replace their credit cards1, with just over a third of users in the U.S. (36%) being repeat customers – utilizing BNPL services once a month or more2. As of April 2021, about 5.8 million Australians have a BNPL account, while 38 percent of people in Singapore utilize BNPL for frequent purchases.3

And why is BNPL so popular? 47% of users take advantage of BNPL plans because they want to avoid paying credit card interest. Other reasons included their friends using BNPL, credit cards being maxed out, and feeling more responsible when they break large purchases into smaller payments2. In Europe, “BNPL options are projected to see the largest gains in usage [in e-commerce] in the next three years and almost double their share until 2024, accounting for almost 14% of the spending on e-commerce in Europe.

With BNPL on its rapid ascent, where does that leave credit cards? Since the start of the Covid-19 pandemic, the value of credit card transactions in the United States has dropped by approximately 11%4. You could argue that consumers were spending less thanks to job loss and closed stores amid economic uncertainty, and there may not be definitive data yet to suggest they are shifting spending from credit to BNPL, but data does suggest “a universal decline in credit card ownership,” particularly among Gen Z consumers, half of whom don’t even own a credit card4. As the revenue streams of big banks in certain regions dry up thanks to loss of interchange and other card-related fees, the credit card industry is looking for ways to offset those declining transactions. Rather than looking at BNPL as direct competition, banks and other traditional lenders can look at BNPL as an opportunity.

“As the digital word spins faster, people expect financial services to be aligned with their busy and demanding lifestyles. The wide adoption of [BNPL products] being universally integrated into purchases reduces friction, grows sales, and gives consumers more options to improve the customer experience. Banks that ignore this market dynamic risk missing out on the opportunity to engage with future generations of borrowers.”4

Lending United

How can BNPL and credit cards work together then? Is there an ideal future state where each space merges together to offer consumers the best of both worlds? There are pros and cons to traditional credit card lending as well as Buy Now, Pay Later plans – depending on a particular consumer’s perspective, risk comfort level, cash flow needs, credit history, etc. If banks were able to include checkout purchase options in their digitization strategy4, they could help ensure longer-term growth potential – perhaps giving users options to pay in installments via credit card or offering lower interest rates. Some consumers may not be aware that the traditional BNPL model shifts the interest fees away from the consumer and onto the merchant, offering a mutually attractive option for both (less perceived interest on the part of consumers, and larger carts with less abandonment for merchants). But, in part as a result of the apparent lack of interest, some 30% of BNPL users trust BNPL providers more than credit cards when it comes to fair business practices.5  

BNPL providers could protect consumers with a blended approach – keeping the simplified lending consumers love (particularly at checkout) but offering additional rewards or perks and ensuring that consumers are able to actually build credit with their use of installment payments. And the rewards of convergence would be worth it as the runway of opportunities is not getting any smaller – a study by Mastercard showed that “43% of consumers in Asia Pacific are willing to increase their spending by at least 15% if allowed to pay in installments.” Meanwhile a research study conducted by Coherent Market Insights showed that the global BNPL market will grow to upwards of $33.6 billion by 2027 (a massive increase from $7.6 billion in 2019)6. To capitalize on this growing market (and those consumers who want to increase their spending) MasterCard invested in Pine Labs, an Asia-based BNPL provider. The partnership aims to bring new installment plans to Asia, with a solution rolled out earlier this year in Thailand and the Philippines, followed by Vietnam, Singapore and Indonesia. Credit, debit and bank cardholders in the region will subsequently have access to installment plans for both online and bricks and mortar merchants upon checkout.

With more than half of the world’s consumer borrowing happening in the Asia Pacific region, there are massive opportunities for fintechs to offer shoppers what they want – namely flexibility and convenience. In a press release from earlier this year, Sandeep Malhotra, Mastercard’s Executive Vice President, Products & Innovation, Asia Pacific outlined the benefits to both consumers and merchants. Flexibility and cash flow management for the former; an increase in sales and reduction in cart abandonment for the latter. And of course, the development of an omni-channel solution benefits MasterCard too: “Installment options complement Mastercard’s wide range of payment programs and align completely with Mastercard’s mission of fostering an integrated, inclusive digital economy and delivering great checkout experiences with payments that are secure, simple and smart.”7

MasterCard isn’t the first credit card company to think outside the box (and they won’t be the last). American Express launched a BNPL-style service in 2017, with its cardholders utilizing installment payment plans for nearly $4 billion in purchases. The “Pay It, Plan It” program directs AMEX to pay a card transaction right after it’s made through a linkage with the consumer’s bank account, and then permits consumers to turn that card transaction into a short-term installment plan for a small fee8. As mentioned, traditionally the merchants pay these types of fees, meaning AMEX is shifting this cost to the consumers – another example of the many ways the term BNPL is no longer a one-size-fits-all product.

By utilizing some of the most-loved aspects of BNPL, credit cards can help ensure their ongoing relevance. And at the same time, evolving their lending methodology and risk decisioning processes could help them widen their net. For example, “alternative data can be used to better underwrite loans for consumers who fall outside of traditional credit metrics,” like the unbanked or underbanked (who usually love BNPL), particularly in regions where it’s very difficult for the average consumer to build credit because spending profiles have changed9. Using technology to better access and aggregate real-time data to make better credit decisions can also potentially provide an easier intersection of BNPL and credit cards – better data and instant decisioning means less risk, allowing providers to offer more personalized payment plans. As Jim Marous put it, “financial institutions must deliver innovative credit options, on-demand, in an almost instantaneous manner” to capture the hearts (and wallets) of their target audience10.   

A Match Made in Fintech Heaven

The future of BNPL is here – in fact, it changes almost daily. And the credit card industry can clearly capitalize on that on a wider scale, if the success of the regional, niche partnerships that MasterCard and AMEX offer are any indication. Not only does a thoughtful marriage help both industries continue to benefit from the incredibly large runway of available market share, it helps merchants and consumers too. As we strive for more inclusion in this increasingly global, diverse world, BNPL and credit cards have the opportunity to give more viable lending/payment/purchasing options and more protection to all types of consumers, especially the unbanked and underbanked. And what better way to help stimulate the global economy than being sure that everyone has a chance to participate in it?

For more information on what industry influencers say about the future landscape of BNPL, read our latest blog.


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Why Customer Experience is so important in financial services, and how a unified decisioning platform can help

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Why Customer Experience is so important in financial services,
and how a unified decisioning platform can help

Enhancing Customer Experience Across the Entire Lifecycle

Personalized experiences are everything these days, and the world of financial services is no exception. We expect our Netflix recommendations to be spot on, we download apps to preview what a new hairstyle would look like, and we trust our Instagram feeds to offer up relevant ad content… but some traditional financial services institutions are missing the boat on personalized, meaningful customer experiences. And their growth is suffering as a result. Why? In part, because legacy systems (including data infrastructure and decisioning platforms) just can’t handle making more personalized offerings. To truly enhance the customer experience from end-to-end, financial services institutions, both big and small, need to look to an all-in-one, Financial Brand shares, “No matter what the future brings, financial institutions need to develop a strategy that can recession-proof and future-proof their current business models. The banking industry has a once-in-a-generation opportunity to transform legacy business models to become more competitive and more resilient during economic upheaval. By integrating data, analytics, advanced technologies, automation and an up-skilled workforce, banks and credit unions can become more future-ready and agile in a crisis.” 

CX: The Rise of Instant Everything

So, what exactly does the oft-overused term Customer Experience really mean? And why is it so critical to an organization’s success? Broadly, customer experience is the impression your customers have of your brand and your solutions as they interact with you – at all stages of the buyer’s journey, from first view of an ad or consuming your content all the way through to purchasing, onboarding and renewals. Customer experience matters in everything we as individuals consume – think about how you feel about your favorite grocery store, smartphone, hair products or exercise program (is Peloton actually a cult?). Now think about how you feel about your banking apps, your credit cards, your mortgage company, your last auto financing application. Are those everyday financial transactions memorable (in a good way)? Do you feel seen? Do you feel like your needs are being met in a personalized way?

Consumers want instant answers and tailored offers with their lending/banking experiences, with far less waiting and paperwork. With the rapid increase in digital-only banking and fintech innovations like buy now, pay later (BNPL) and embedded financing apps, providing anything less than a stellar customer experience means fewer repeat customers and a decrease in brand value.

Accenture reports that 5% of traditional banks’ revenue is at risk as millions of consumers are enticed by the transparent, tailored offerings of fintechs and neobanks. A 2020 survey of credit union/bank marketing leaders found that personalized approaches are the most effective for engaging people and expanding share-of-wallet. But 44% of those same organizations only send a couple of targeted marketing emails per year. Why? “Limited data insights make it difficult to truly understand a consumer and what they need in the moment.”

Data + AI-Powered Decisioning Technology = More Satisfied Customers

What does your risk decisioning platform have to do with the customer experience? In one word… everything. While some financial institutions are still using siloed environments and separate vendors or partners for data, decisioning workflows, analytics models and business insights, the more agile, adaptable organizations are looking at unified, all-in-one decisioning platforms. One solution that integrates real-time data, advanced analytics, artificial intelligence and machine learning (AI/ML), and decisioning automation can help accelerate digital transformation for a more customer-centric experience. With a unified solution, you can:

  • Make smarter, more accurate decisions
  • Shorten the product development lifecycle and get new products/offerings to market faster
  • See real-time views of decisioning and performance data to uncover actionable business insights
  • Create streamlined user experiences across the customer lifecycle
  • Scale and grow your business to respond to market trends and consumer demands (with fewer growing pains for your loyal customers along for the ride)
  • Democratize data access for more holistic views of your customers
  • Optimize pricing and product offerings
  • Expand your customer relationships with personalized upsell/cross-sell offers

McKinsey doesn’t hold back: “Predictive customer insight is the future.” Their article on ‘Future of CX’ predictions states that “those with an eye toward the future are boosting their data and analytics capabilities and harnessing predictive insights to connect more closely with their customers, anticipate behaviors, and identify CX issues and opportunities in real time.” While customer success teams and feedback surveys will always have a place in understanding the consumer experience, it’s clear that real, actionable data that can be analyzed in real-time is a game-changer.

But accessing, integrating, and analyzing data is not the only challenge facing financial institutions who desire a better experience for their customers. In today’s ultra-competitive landscape, doing so at speed is critical. As the Financial Brand shares, “Speed is a competitive weapon. The ability to see market trends, adjust strategies, innovate, create new solutions, make tactical decisions, and deploy resources quickly provides a business advantage… Banks and credit unions can no longer respond to opportunities and challenges with a legacy banking timetable.” But being able to adapt your offerings and pivot to new products or strategies quickly is next-to-impossible without an integrated, unified solution.

What to Look for in a Unified Solution

If you’re overwhelmed by the idea of choosing yet another technology partner, don’t fret. We’ve looked at some key factors to consider when evaluating decisioning platforms.

  • No-code Management: Can you easily integrate systems, change processes, and launch new products, without relying heavily on your IT team and/or your technology vendors? Is your team empowered with a low/no-code UI (see, the customer experience matters in absolutely everything!) that can offer you things like pre-built data integrations and drag-and-drop functionality?
  • Connected Data: Do you have easy access to both real-time and historical data, including alternative data sources? Can you centralize your data sources (goodbye silos) so users can more efficiently manage various data sets across the credit lifecycle and make smarter decisions?
  • Centralized Control Across the Lifecycle: Can you bring together data and decisioning to better manage identity, fraud, and credit decisions across the entire lifecycle of the customer? Are you able to efficiently connect all systems to fully understand customer needs and personalize user experiences? When consumers are expecting seamless experiences, with tailored financial services and protection from fraud, can your technology deliver?
  • Auto-Optimization: Does your decisioning platform get more accurate each time decisions are made? Are you able to see how your current risk models are performing, and can you respond to these performance shifts once you’ve spotted them? Instead of relying on humans or individual systems to uncover opportunities for improvements, can you connect all systems and power a continuous feedback loop that keeps optimizing your decisions?
  • Ability to Scale: Your systems may be working well enough for now, but as the industry continues to grow more and more competitive… can they adapt and scale with you in the future? Does your decisioning solution simplify your growth or inhibit it?

A unified decisioning platform not only powers more accurate decisions across the entire customer journey, but it enables rapid growth and innovation opportunities. Instead of waiting for vendors to make workflow changes or sifting through siloed sets of data, you can spend more time focusing on what matters – your customers. Adapt as the market shifts, diversify to meet your customers’ needs, personalize offers to encourage engagement and brand loyalty. The opportunities for enhancing the customer experience are endless – and with a holistic, unified view of your data and decisioning, you don’t have to compromise your risk strategy to do so.

Discover more benefits of unified access to AI-powered decisioning and the data that fuels it.

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Tartan

Partners

Tartan

Real-Time Income, Employment Verification of End Users

Key Benefits

  • Making Income and Employment Verification Seamless. Using income and employment data, lenders know more about their users and club with other data sources to develop a holistic credit profile. Get 60+ data points in a standardized format from the HRMS system.
  • Ten times reduction rate in end user journey TAT. Make use of multiple data points to pre-fill the end-user journey, which means less time spent by end users to input details without uploading numerous documents—leading to fewer user drop-offs and a seamless onboarding journey.

“Working with TartanHQ has helped us fetch and verify end users’ income and employment data in real time, making our underwriting model quicker and enabling better decisions.”

ASHWIN, PRODUCT LEAD, M2P

One-Click Access to Work Data From the Source-of-Truth

With Tartan’s payroll and workforce connectivity and digital identity APIs, we are making the process of employment and income verification seamless and credible using technology.

We enable businesses like digital banks, lenders, insurance, upskilling, tax platforms, and marketers to create and distribute products efficiently by partnering with HRMS, HRIS, work platforms, and payroll service providers.

We truly believe in financial inclusion for all and solve for multiple categories across salaried, freelance and gig workers. With the help our payroll APIs, our customers are able to target new segments, reach broader geographies and enter new markets.

About Tartan

  • Services

    • Payroll pull
    • Payslip OCR
    • Work history verification
    • Identity verification
    • Utility verification
    • Vehicle verification
    • Financial Verification
  • Countries Supported

    • India

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Provenir Named to Credit & Collections Technology Power List of Top 20 Companies in 2022

NEWS

Provenir Named to Credit & Collections Technology
Power List of Top 20 Companies in 2022

The annual Power List compiled by Credit Connect features the most influential and innovative companies within the credit and collections technology sector.

Parsippany, NJ — Dec. 22, 2022 — Provenir, a global leader in data and AI-powered risk decisioning software for the fintech industry, today announced it has been named to the Credit & Collections Technology Power List of top 20 companies in 2022.

The Power List is an annual guide to the most influential and innovative companies within the credit and collections technology sector. The list is compiled by Credit Connect and is based upon a company’s performance in the Credit & Collections Technology Awards over the past five years whereby a company has been awarded points based upon wins and finalist status.

Provenir was named the winner for “Best Credit Risk Solution” in the Credit & Collections Technology Awards 2022; Provenir also won in this category in 2021.

“Being named to the Credit & Collections Technology Power List is a great honor for Provenir,” said Provenir’s Frode Berg, General Manager, Europe. “We are proud to work with industry leading financial services providers and fintechs to deliver AI-powered data and decisioning, empowering organizations to innovate further and faster than ever before, driving the continuous optimization they need to power growth and agility without increased risk.”

The complete Credit & Collections Technology Power List can be found here.

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The Ultimate Guide to Decision Engines

What is a decision engine and how does it help your business processes?

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Meeting Customers “Where They Are” Through Financial Inclusion and Hyper-Personalization

NEWS

Meeting Customers “Where They Are”
Through Financial Inclusion and Hyper-Personalization

The current economic climate poses significant challenges for both businesses and consumers – a situation the financial services industry must recognize as both a threat and an opportunity. With consumers’ shifting economic situations, risk profiles are changing, necessitating that financial services companies meet their customers where they are by providing a higher level of personalization than ever before. It also means changing the traditional business models that have excluded a significant part of the population. 

In this Finance Digest article, Kathy Stares, Provenir’s EVP, North America discusses how the use of prescriptive analytics that leverage alternate data, machine learning and AI can help organizations lean into financial inclusion and hyper-personalization.

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The Ultimate Guide to Decision Engines

What is a decision engine and how does it help your business processes?

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Data Your Way – Streamlining Your Data Strategy

ON-DEMAND WEBINAR

Data Your Way –
Streamlining Your Data Strategy

Book a Meeting

Whether you’re a product manager or part of the wider risk team, you know that access to the right data at the right time is vital to product—and business—success.

To launch new products and optimize existing ones you need a streamlined data supply chain that gives you the power to make smarter decisions across identity, fraud, and credit decisioning.

If you struggle with:

  • Sourcing the right data
  • Managing multiple vendors
  • Building out your data supply chain
  • Integrating data into your decisioning technology
  • Lacking the agility to adjust your data strategy on your timeline

Then watch our on-demand webinar as we cover the steps needed to ensure data strategy success across any financial services product offering.

Our team of data specialists covers:

  • Developing your data strategy to optimize decisioning for financial products
  • Streamlining your data supply chain to drive increased agility and faster speed to market
  • An exclusive demo showing how Provenir Data solves your data challenges and puts the power of data in your hands

Speakers:

  • Kerry Cleary

    Global Head, Data Partnerships

  • Michael Shurley

    VP Presales Solutions

  • Sam Kimish

    Head of Product Success


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Smile API

Partners

Smile API

One Trusted Source for Employment Data in Asia

Key Benefits

  • Access to Employment Data through one API. Smile API provides user-authorized access to recent, comprehensive, verified employment data that is accessible in real-time from employment documents, HR and payroll systems, gig economy platforms, and social security systems through a Singe API.
  • Make better credit decisions, increase conversion, reduce risk. Unlike traditional sources of credit data like credit scores, blacklists or submitted documents, we provide greater coverage, real-time access, and the most recent, comprehensive, identity and employment data that is available in the market today.

Smile Provides Employment Data Across Platforms and Employers, all Through a Single API

We are building infrastructure for alternative credit data by allowing borrowers to easily share their employment and income information to lenders. We aggregate and unlock previously siloed employment and income data coming from employment documents, HR and payroll systems, gig economy platforms, and government systems. Everything is done in real-time and with their consent. 

Resources

About Smile API

  • Services

    • Capture and verify a user’s identity including their name and contact information
    • Capture and verify income and employment data in seconds. Retrieve them straight form the source, as well as receive other historical employment data included in their file.
    • Get access to other alternative credit data points to better assess your user’s credit worthiness such as their gig transaction data, performance ratings, insurance contributions and outstanding liabilities.
    • Automatically capture of data from scanned or photographed employment documents
  • Countries Supported

    • Philippines
    • Indonesia
    • Singapore

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