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

Written by Amy Sariego on . Posted in Blog.

BLOG

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

Written by Amy Sariego on . Posted in Blog.

BLOG

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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Optimizing Your Data Strategy

Written by Amy Sariego on . Posted in Blog.

BLOG

Mo’ Data, Mo’ Problems:
Choosing the Right Data

Why the right data, not more data, is key to optimizing your data strategy

Big data and the term ‘data strategy’ gets thrown around a lot – but what is a data strategy when it comes to financial services, and how can you optimize it for more accurate, smarter risk decisioning? The answer isn’t more data, it’s the right data. Read on and discover how to choose the right data for your business use case and why optimizing your data strategy is key to your decisioning success.

Make Your Data Work Smarter, Not Harder

In our increasingly digital society, it seems like everyone is focused on more. More data, more choice, more speed, more competition, more options (how many different entertainment streaming services are there now??). More, more, more. So, our rebel yell is that when it comes to your data strategy, it’s not about more data, it’s about the right data, at the right time. According to IDC, “this year alone, over one hundred thousand exabytes of data will be 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. The data is there, but it’s an incredible amount of wasted effort if you don’t know which data sources to use when.

74% of decision-makers struggle with their organization’s credit risk strategy because data is not easily accessible.

2022 GLOBAL FINTECH AGENDA, POWERED BY PULSE

When developing a data strategy for your financial services offerings, you need to look for ways to minimize costs and maximize innovation. And that means being able to select only the data you need, exactly when you need it, in order to make more accurate decisions across credit, identity, and fraud. According to McKinsey, “industry leaders tap multiple internal and external data sources to improve the predictive power of credit signals… both the internal and external data sources used in a credit-decisioning model will affect the decision quality.”

What can the right data do for your decisioning strategy?

As McKinsey put it, “Data marketplaces enable the exchange, sharing, and supplementation of data, ultimately empowering companies to build truly unique and proprietary data products and gain insights from them.” When it comes to risk decisioning specifically, that translates into several key benefits – and competitive advantages:

  • Improved customer experience: Ensure a frictionless digital experience for low-risk customers and enable data-driven actions on potential risk in real-time
  • Improved accuracy in your decisioning: The right data at the right steps in your decisioning processes across the customer lifecycle means more efficient, accurate risk decisions
  • Minimized data costs: Reduce the time/effort/resources necessary to source, build and maintain data integrations if all the data you need is right at your fingertips
  • Scalability: With the right data sources on both a local and global level, you can get new products to market in new regions faster by duplicating and iterating your data strategy
Types of data that are critical to optimizing your decisioning strategy across the lifecycle:
  • Identity Data: Verify identities and documents for better onboarding compliance, prevent identity fraud, and be sure that you are protected with ongoing due diligence data.

      Includes: KYC/KYB, PEPs/sanctions, document verification, synthetic ID fraud
  • Fraud Data: Identify potential first-party and application fraud in real-time to proactively detect/prevent fraud and reduce losses; reduce false positives by leveraging signals from mobile, email, behavior, device, IP, social and other fraud data sources.

      Includes: Email and mobile data, global fraud intelligence, social validation, device data, IP, and geolocation
  • Credit Data: Minimize credit exposure and loss by leveraging credit bureau, open banking, and alternative data sources. Ensure optimized credit onboarding and add value throughout the entire customer lifecycle with dynamic customer risk profiling, mitigate collections and optimize customer lifetime value.

      Includes: Credit bureau data, business data, open banking and alternative data including social media, rental payments, travel info, utilities and more
Data supply chain challenges and how to overcome them
Choosing the right data can seem daunting, but it’s critical to have an optimized data supply chain, with the right data in the right place, in order to deliver the most effective products to your customers. And depending on the type of financial product you are offering there are regional regulations to consider, third-party vendors required, technology requirements and more. These are some of the most typical challenges known to slow down deployment of even the most well-thought-out data strategies:
  • Identifying relevant local data sources
  • Negotiating multiple contracts
  • Complying with varying regulations
  • Ensuring data privacy for different regional requirements
  • Normalizing data formats
  • Building and maintaining integrations
  • Supporting global strategies
But you can overcome these challenges by ensuring you have the right data for each and every product offering you have. How? Work with a partner that provides an all-in-one data solution. Building your own data supply chain, for whatever your use case, is possible of course, but it’s time-consuming and resource intensive. If you want to work with a partner look for a data solution that offers:
  • One data contract that provides access to multiple data sources
  • A single API to replace numerous integrations
  • A wide variety of data types and sources, including alternative data
  • Expert data source curation customized to your needs, that can be easily modified as your needs evolve
  • Simplified, no-code data supply chains that non-technical users can understand and control
  • Global data access, as well as local sources, to ensure success of both regional tactics and the ability to iterate and expand to new markets
  • Seamless integration into your decisioning technology to ensure accurate, smarter decisions
If you’re a Buy Now, Pay Later provider, or are thinking of diving into the fray, check out our blog highlighting specific ways to optimize your data supply chain for BNPL.

Read the Blog

Simplifying your data supply chains (sourcing, building, integrating, and maintaining data sources and connections) and optimizing your data strategy is critical to continued success – and your competitive advantage. Don’t let yourself be overwhelmed by the immense variety of data available out there – remember, the right data is much more important than more data. Accessing the right data at the right time means enhanced risk models, strengthened onboarding processes, more accurate decisioning across the lifecycle, and optimized customer experiences.

For further reading, check out these articles that may be of interest:

What the Data-Driven Bank of the Future Looks Like

– The Financial Brand

Designing Next-Generation Credit-Decisioning Models

– McKinsey

The Data-Driven Enterprise of 2025

– McKinsey

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Guest Blog: Three Best Practices for Implementing Digital ID Verification

Written by Amy Sariego on . Posted in Blog.

GUEST BLOG

Three Best Practices
for Implementing Digital ID Verification

  • Christina Luttrell, Chief Executive Officer at GBG Americas

A recent report showed that 86% of businesses view identity verification as a strategic differentiator, allowing them to capitalize on digital adoption while delivering a seamless customer experience. Consumers who don’t trust the digital identity verification process are more likely to use guest checkout (54%) and less likely to keep a payment card on file (43%), thereby creating a drag on profits while compromising the end-user experience.

The following best practices can help fintechs locate, verify and approve new customers without friction or fraud while streamlining the customer journey.

Onboarding in The Digital Landscape

Being successful in a digital environment means being able to onboard and verify users in a purely digital way. This means doing all the required elements, such as KYC, AML, checking against sanctions lists, etc., in a digital-only environment, which can be challenging.

This means needing to design a UX that is inclusive of digital identity verification at its core, with access to multiple verification layers that can be deployed in each required scenario. Fintechs make money by people utilizing their service. Providing a digital experience that opens the door to more good customers—while also meeting regulatory requirements—is a goal for all fintech providers.

A robust ID verification solution gives fintechs the confidence to onboard more legitimate customers faster, with nominal friction, while staying compliant.

Data Diversity & Consortium Networks

Central to the requirement for effective digital identity verification is data diversity. Incorporating other identity verification data sources is essential, as the more indicators are used, the more robust the system is compared to a traditional system reliant on credit checks, which can be breached.

The other consideration is data transparency – data must be sourced and explained, as a critical requirement for ongoing regulatory compliance, and justify decisions to customers.

This is where the idea of consortium networks, where data is shared between a large network of interconnected parties, becomes highly important, as they enable new account openings at different institutions to benefit from fraud data and learnings elsewhere in the ecosystem, securing the whole market more effectively.

Ongoing Verification

Onboarding is an important element of fraud prevention, but ongoing verification is necessary, which is the authentication part of the equation. Opening a fraudulent account is a risk, but account takeover of an existing account is also a significant risk, as payment account fraudsters have access to make payments and view transaction history and payment details.

The requirement is for fintechs to design strategies that ensure that verification is carried out continuously. This could be when an unusual transaction is made, or when a new payment method is set up, or in any number of given scenarios. 

Given what’s at stake, if fintechs fail to implement robust systems based on more than just point solutions for ID document scanning, they will struggle to deal with evolving fraudster tactics. For this reason, the industry could see the continued fusing of physical and digital attributes for verification, such as taking name, address, date of birth, etc. Only by taking a multi-layered, customizable approach will banks achieve the best anti-fraud and customer experience outcomes.

Visit IDology.com to discover innovative solutions that streamline customer acquisition, deter fraud, and drive revenue.

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Data Now, Fewer Losses Later: Optimize Your BNPL Data Strategy

Written by on . Posted in Blog.

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Data Now, Fewer Losses Later:
Optimize Your BNPL Data Strategy

(Buy Now, Pay Later (BNPL)) products have exploded across the globe, offering a new spin to point-of-sale (POS) financing for both consumers and businesses. From 2019 to 2021, BNPL loan originations increased by 970% from the top five lenders alone, and the industry continues to expand to include new verticals such as auto repair, grocery purchases, airline ticketing, and more. Consumers are beginning to rely on BNPL for everyday costs in order to help manage their cash flow. But none of this would be possible without data – more specifically, a strong data supply chain. 

If you’re a BNPL provider, the data supply chain is the powerhouse for your solution. When you have the right data, you can better determine risk, protecting your business against fraud and loan default. 

BNPL data strategies look beyond traditional data like credit scores and use alternative data to make credit more accessible and faster to approve without increasing your risk. While this allows you to expand your customer base in a secure way it also adds complexity to your data needs. So, how do you build a BNPL data supply chain strategy that gets the right data to the right place exactly when you need it?

Building Your BNPL Data Supply Chain

We know every potential BNPL customer must go through a process, but what does that process look like? Each step is built with different data checks that tell your decisioning engine whether to move that customer forward. An optimized data supply chain pulls only the necessary data needed for a customer at each checkpoint – data that comes from your data integrations and data partners. 

An optimized data supply chain has these hallmarks:

  • Multiple steps with distinct requirements
  • Multiple checkpoints at which consumers either pass or get denied
  • Steps that increase in complexity and cost of data
  • No unnecessary data is exposed and paid for before you need it

Launching with an MVP:

Are you a startup launching your first BNPL solution? A finserv expanding your product line? Maybe you’re an online shop looking to reach more customers. Whatever the case, when building a new data supply chain for your BNPL offering or optimizing an existing one, you should begin with your minimum viable product (MVP) – the basics you know you need to launch your product. 

An MVP has the least amount of checks in the process, pulling in the least amount of data. You might want to begin with an MVP if you want to:

  • Go to market quickly
  • Minimize the cost of development
  • Analyze basic performance to optimize more complex iterations in the future

To launch with an MVP approach you’re going to need data to support three key areas: 

  • Regulatory compliance checks like KYC/AML
  • Identity verification 
  • Credit risk

 An MVP for consumer lending could look like this: 

Step 1: KYC

The first step of the process is validating the most basic data to confirm the customer’s age, address, and identification. If you can’t verify a person’s ID, you certainly can’t lend to them. 

Step 2: Fraud Prevention

The second step digs deeper into a person’s identity to ensure they are who they say they are and help prevent fraud. There is a wide variety of data you can pull for a fraud check, including email address verification, if a SIM card has been swapped, and other behavioral and alternative data. If not all of this information matches, it could be a sign of attempted fraud, and the person would be rejected. 

Step 3: Credit Risk

The final step is to check creditworthiness. A bureau check is done through a soft credit check that grants you access to a consumer’s credit score without impacting it. With an MVP, BNPL providers would likely reject anyone with a score below a certain threshold or someone without enough credit history to have a score at all. If a person has made it through the process, the data is assessed holistically by a decisioning engine to determine whether and at what terms to grant the loan.

Beyond the MVP: Optimizing Your Data Strategy

Beyond the foundation needed for an MVP launch, you can optimize your supply chain based on your company’s risk appetite and goals. Before updating your data supply chain it will help to: 

  • Analyze success against your goals
  • Identify weak points in your data strategy

While you may want to initially launch your BNPL solution using an MVP, as you grow and want to add complexity, you can incorporate new data points and data partners. Think about the kind of customer you want to capture, as well as business goals and preventative measures you may want to take, and ask yourself:

What percentage of fraudulent applications is our current process letting through? Is this in line with our business goals? If not, look to:

  • Add additional fraud checks on existing steps
  • Add standalone fraud prevention steps to the process
  • Amend data sources to optimize as you go

Are we offering the most competitive terms to our customers? How can we improve conversions? For competitive edge and increased personalization, use data such as:

  • Behavioral trends
  • Geolocation
  • Activity and usage 

How effectively are we reducing defaults? Are we filtering out non-viable customers at the right point in the process? Make sure your flow features:

  • Prescreening
  • Scoring
  • Additional data checkpoints on existing steps

For BNPL providers that want robust data supply chains across credit, identity, and fraud while maximizing efficiency, an optimized flow could look like this:

BNPL Data Supply Chain 2

Prescreening 

Prescreening breaks down the identification verification steps even further, making sure the minimum requirements are met. It’s a faster, more efficient way to filter out unqualified applicants without using unnecessary time and resources. 

What does prescreening look like in an optimized supply chain? Say you have a person under 18 – they’re not legally allowed to take out a loan, so their application would be rejected. In an MVP, someone that can’t even use the product would still have their identity verified, but it’s a waste to run those checks, since they’re not a viable customer. Optimization ensures you expose only the data you need at each step.

Scoring

Scoring pulls supplementary data that helps paint a clearer picture of a consumer’s risk. This includes mobile device data, additional fraud checks, or any other kind of alternative data you want to feed into your decisioning tech. 

Why include scoring in your process? Again, it comes down to building your process for optimal efficiency and minimal cost. At this point, you would know if the customer was viable, who they are, and what their financials look like – this is all straightforward data to pull. Scoring adds behavioral information that is more time-consuming and costly to analyze and should be incorporated only when everything else checks out. 

Ultimately, the more relevant data you have, the more accurate your decisions will be, the better you can predict future defaults, the easier it will be to identify upsell and cross-sell opportunities – whatever your business goals, the right data can help you get there. Optimizing your consumer BNPL supply data chain is dependent on finding the ideal number of checks and steps to accurately determine creditworthiness and risk, while keeping the process fast and efficient.

Ready to launch and expand your BNPL products? Look out for these data supply chain challenges

As BNPL products continue to grow around the world, new markets have emerged, and with them new challenges. To build a global supply chain, you have to know regional regulations, vendors, tech requirements, and more. Some of the challenges that can slow down deployment of your data strategy include:

  • Identifying relevant local data sources
  • Negotiating multiple contracts
  • Complying with varying regulations
  • Ensuring data privacy for different regional requirements
  • Normalizing data formats
  • Building and maintaining integrations
  • Supporting global strategies 

BNPL is a fast-moving industry, so it’s also important to ensure your supply chain can be easily iterated on to incorporate evolving legislation and market demand. 

Data Powers BNPL

Regardless of trend, customer type, or region, your BNPL solution is powered by data. Diverse data sources pulled at the right time in the right order is the calling card of an optimized data supply chain. And an optimized data supply chain feeds your decisioning engine the information necessary to give you a smarter decision every time. 

Building a data supply chain on your own, however, can be a huge undertaking and an even bigger headache. Instead, consider choosing a data partner that can build it for you, while connecting you to the integrations you need to grow your BNPL business. 

Ideal features include:

  • One data contract that gives you access to multiple data sources
  • A single API to replace numerous integrations
  • A wide variety of data types and sources, including alternative data
  • Expert data source curation customized to your needs
  • Simplified, no-code data supply chains that non-technical users can control
  • Global data access
  • Integrates into your decisioning technology to ensure seamless and smarter decisions

Do you want your data on-demand? Meet Provenir Data.

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Innovations in Risk Decisioning Fuel YapStone’s Rapid Global Expansion

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Innovations in Risk Decisioning
Fuel YapStone’s Rapid Global Expansion

The global sharing economy continues to transform the online payments landscape, as we know it.

We spoke to YapStone, a payment platform taking the world by storm, and asked them how their new credit decisioning model has helped expand their company at a rapid rate, on a global scale. Through advanced analytics, model development has taken place quickly and effectively for YapStone. Here, they talk us through how credit scoring, open banking data, and credit risk models have facilitated their growth, allowing them to sit amongst industry leaders.

In a time when virtually anyone can sell goods or services on the internet, real-time merchant onboarding and risk and fraud monitoring capabilities have become imperative.

At YapStone, we know this very keenly because the bulk of our users are not simply selling products to strangers they will never meet – they are inviting consumers into their homes.

Twenty years ago, we couldn’t have imagined we’d be comfortable inviting complete strangers to stay in our home for extra income, but thanks to vacation rental marketplaces like HomeAway, Airbnb, and Kigo, “living like a local” has become the preferred way to travel. As a result of this undeniable trend, YapStone now processes about $18 billion (and growing) in electronic peer-to-peer transactions every year.

Further challenges to marketplaces are emerging with the rise of Alternative Payment Methods (APMs). Consumers in different countries or regions have their APMs of choice, using them to pay securely with their local currency. As the payment partner, we have to ensure that these methods suit our customer’s lifestyle, and that they can securely pay using their preferred payment methods in their local currency, while sellers receive the funds seamlessly in their local currency.

YapStone has been able to capitalize on the growth of apartment and vacation rentals, where the average ticket size is large and the risk is high, by developing proprietary technology focused on reducing the risk of fraud and loss to our marketplace partners. YapStone’s trust and safety solutions are highly flexible and designed to service all marketplace types, allowing us to diversify our portfolio and grow our business beyond apartment and vacation rental marketplaces.

YapStone is unique in that we offer a full service, end-to-end payments acceptance, customer service, and risk management solution, including instant and advanced payments, to our marketplace partners.  Therefore, it is a necessity for YapStone to verify the traveler and authorize their payment method, as well as verify the vacation property’s existence and ownership. Our goal is to deliver trust and safety to our marketplace partners, allowing them to spend more time focused on growing their business, while leaving risk management to the expert team at YapStone.

YapStone uses a layered, risk-based approach focused on the persona of any particular client interaction.  Within the persona, we are continuously monitoring the purchaser of the good or service, the payment instrument being presented, and the asset or property they are renting.  To accomplish this, YapStone utilizes proprietary data science and predictive analytics augmented with 3rd party data to achieve the most accurate risk scoring.

Given our scale, and the risk associated with high average tickets and the speed at which fraud can happen, it was critical for us to choose a tool that allows a risk analyst to react quickly to an escalating threat.

In 2017, we selected Provenir as a key strategic partner in the development of YapStone’s next-generation risk decisioning platform. The tool provides the ability to house our proprietary underwriting and fraud models, serve as the hub for our third-party risk vendor integrations delivering powerful adapters to augment our proprietary risk methodologies and data, and conduct A/B and regression testing for new or proposed model changes, all delivered through a simplified user interface that doesn’t require a PhD in computer science to use.

We maintain a highly competitive edge over other payment facilitators because of the way we mitigate risk for our marketplace partners and assume the liability for each transaction. Using our proprietary technology in partnership with Provenir, YapStone is able to provide marketplaces with high levels of automation for merchant onboarding and risk management aimed at improving speed to revenue and reducing losses for our clients.

We have a very exciting future ahead of us, made particularly bright by having partners like Provenir who help us deliver innovative solutions to our existing customers and new faces. As we expand into new territories, the demand for innovation in risk decisioning will be high. The team at YapStone looks forward to staying on the forefront of this new wave of marketplace payments.

Companies invest lots of time and money developing risk models to figure out which  customers are the best bets for loans and credit, including auto lending, mortgages, credit cards, BNPL and more.

Operationalizing these models,  developed in tools like Excel, SAS, Python, and R, within risk decisioning processes often turns out to be challenging. This is especially true with complex models built in R. Lots of risk decisioning ‘solutions’ demand that you manually translate the R model (or any other model that you are using) into code that it can understand. You need high-priced programming resources and lots of time to connect the R model to the risk decisioning process.

It’s much more efficient to use a risk decisioning solutions which are model-agnostic – in other words, a solution that doesn’t care how the model is constructed. Provenir’s AI-Powered Decisioning Platform is a great example of this model-agnostic approach. With this platform, models developed in a variety of tools can easily be imported, mapped, tested and validated using simple wizards.

Provenir automatically generates a list of the data fields; all users have 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.  Automating your risk decisioning not only saves you time and money – it improves your decisioning accuracy and allows you to focus your resources on growing and scaling your business.

The Ultimate guide to Decision Engines

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

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Simplifying the Merchant Onboarding Process with Automation

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Simplifying the Merchant Onboarding Process
with Automation

The Challenges of Manual Merchant Onboarding

Merchant onboarding is a critical process for acquiring businesses that involves acquiring, analyzing, and integrating large volumes of data. However, the manual and time-consuming nature of this process often results in delays and errors. For instance, if data or knowledge of the merchant is lacking, then identity can’t be validated. Compliance with Know Your Customer (KYC) and other governmental regulations has to be determined, as does creditworthiness. This takes days and can still involve a high degree of manual handling. To streamline this process and make it more efficient, automation is the way forward.

Compliance with KYC and Other Regulations

KYC stands for “Know Your Customer” and is a process that financial institutions and other regulated companies use to verify the identity of their clients. This process involves collecting and verifying various types of information about the client, such as their name, address, date of birth, and other identifying information. The objective of KYC is to prevent financial crimes such as money laundering, terrorist financing, and other fraudulent activities.

During the merchant onboarding process, compliance with KYC and other governmental regulations is required. Failure to comply with these regulations can result in fines and other legal consequences. By automating the merchant onboarding process, companies can streamline the KYC process, making it quicker and more efficient, while also ensuring compliance with regulatory requirements.

The Benefits of Automation

Simplified Data Integration

To simplify data integration, acquirers need to access and efficiently handle and analyze all data sources such as bank account information, commercial data, address verification, KYC checks, credit score, and more. To achieve this, a merchant onboarding solution with integration capabilities that can rapidly aggregate data from various sources is required. Non-standard data, such as that from social media, can supplement sources – if the acquirer has the means to get at it and pull out what’s relevant. To achieve this, the best solutions offer pre-built adaptors built on industry standards.

Operationalized Risk Models

Operationalized risk models play a critical role in the merchant onboarding process. They integrate with the other elements that make up the end-to-end merchant onboarding process, ensuring that risk decision-making is not a bottleneck in the process. Technology and model-agnostic solutions can integrate with SAS, Excel, and anything else besides. Business-defined rules lay down the terms and conditions for each merchant and identify exceptions that require further investigation. A visual interface lets business users quickly establish the relationship between the risk model and the automated onboarding process.

Effective management of data is essential to a rapid, efficient merchant onboarding process. Technology for automated risk analytics and decision-making integrated into the onboarding process taps into multiple data sources and systems for a streamlined end-to-end process. To learn more about simplified data integration and operationalized risk models for merchant onboarding, check out our guide on our website.

Learn how you can choose the best merchant onboarding automation solution.

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Machine learning – all a bit ‘Skynet’?

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Machine learning –
all a bit ‘Skynet’?

Machine Learning – Revolutionizing Financial Risk Analysis and Decision-Making

While the concept of Machine Learning (ML) may conjure up images of a dystopian future where machines have taken over the world, much like Skynet in the Terminator movies, the reality is quite different. While Skynet may have been a malicious and all-powerful AI system, Machine Learning is simply a tool that can help us better understand and leverage data. So, while Skynet may have been the ultimate villain, Machine Learning is more like the trusty sidekick that helps us save the day.

Here’s how ML is rapidly becoming a game-changer in the field of financial risk analysis and decision-making:

The Power of Data

Machine learning enables businesses to gather and analyze data faster, thereby arriving at insights quicker. This is because the software program uses pattern recognition to build automatic analytical models, eliminating the need for human intervention.

Dynamic Fraud Detection

Machine learning algorithms can learn from a customer’s previous transactions and use them to identify patterns of behavior, allowing for dynamic fraud detection. This eliminates the inconvenience of manual validation processes while also increasing fraud detection rates, saving considerable costs.

Huge Cost Savings

According to analysis firm Oakhall, global financial services firms could save $12 billion annually through machine learning fraud management. This underscores the tremendous potential for risk analysis and decision-making with machine learning.

Harnessing Machine Learning for Predictive Analytics

To fully benefit from the predictive analytics power of machine learning, financial institutions need a fast, simple way to connect their machine learning application to their credit and lending decisioning processes.

Machine learning is revolutionizing the financial risk analysis and decision-making process. Its power lies in its ability to gather and analyze data faster, dynamically detect fraud, and save costs. By harnessing its predictive analytics capabilities, businesses can unlock its full potential for risk analysis.

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Loan Origination Software Plays Its Part in Banking’s Digital Transformation

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Loan Origination Software
Plays Its Part in Banking’s Digital Transformation

Loan origination — and, subsequently, loan origination software — is at an interesting intersection right now.

At a less institutional level, peer-to-peer lending is expected to grow at a CAGR of 53% between 2016 and 2020. But as lending technology matures, its impact could reduce profits in American banks by $11 billion per year, or roughly 7%.

This evaporation of margins is not a new problem for banks, though it is one that is increasingly disconcerting. In 2012, for example, the share of risk and compliance within general banking costs was 10%, a large portion of overall costs as they were. In 2017, risk and compliance are expected to consume 15% or greater. While costs are rising, it’s hard to actually mitigate risk with incremental risk management improvements. In large part, return on equity in banking often resides below the cost of capital, impacted by capital building projects and fines.

The result: to see increased growth into the next decade, banks are digitizing more processes. This has begun to happen, but a variety of studies — including many on millennial bankers — shows the digital transformation of financial services has not yet fully arrived. Since lending is a huge revenue source for banks across virtually all segments from small business to enterprise, making sure digital loan origination is properly executed is preeminent for many banks now.

As McKinsey has noted, the shift to increased digital transformation focus in financial services came about because of five distinct pressures (paraphrasing here):

  • Changing customer expectations: Consider the rise of mobile and on-demand experiences.
  • More regulations and risk-function effectiveness: Seen in increased regulations in most first-world economies, as well as more fines being dealt since the 2008 crisis.
  • Data management and advanced analytics are hallmarks of competitive banks now: Buzzword or not, we are living in a Big Data era.
  • Disruption: Risk management programs are essential for banks to compete with upstarts — if the upstart makes a big bet and misses, the established bank can favorably reposition.
  • Increasing pressure on costs/returns: And as noted above, risk management doesn’t necessarily deliver in this way on the balance sheet.

As such, rapid-fire, on-demand loan origination programs and software have begun cropping up in the financial services world. Why? When risk decisions are made in seconds, loan origination cycles shorten for the customer. Shorter cycles create positive customer experiences, brand loyalty, connection to the bank and its relationship managers, and continued business. Speed can be good.

But, banks attempting a new approach to loan origination need more than speed. While quick risk decisioning and credit scoring is crucial, a loan origination software program also needs:

  • The ability to process both structured and unstructured data: This would allow for the incorporation of both standard and alternative decisioning, i.e. credit documents vs. items from a loan applicant’s blog.
  • Compliance: Perhaps the most important at the bank level, compliance calculation and True in Lending Act (TILA) disclosures need to be compliant, and documents must be in compliance with the Electronic Fund Transfer Act.
  • “Look for initiatives within easy technological reach:” That’s advice from McKinsey above, and it makes good sense. Some loan origination software barely requires advanced coding anymore, so your IT side can work on more value-add internal projects.

There are other considerations such as ease to operationalize risk models that should be deployed.

Financial services, and especially loan origination have long suffered from a lack of transparency and simplicity. It oftentimes seemed that financial services firms were underserved by technology, or “square-peg/round-holing” the problem. That’s not the case anymore, and loan origination software and approaches are of huge value for established banks as a way to drive a growth culture forward. The crucial step is the right partner for your specific needs.

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Loan Origination in the Golden Age of Instant Everything

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Loan Origination in the Golden Age
of Instant Everything

It may look like the golden age of “instant.” The casual iPhone user can get an Egg McMuffin delivered to their door while it’s still steam-hot in the bag. The average Twitter user can use their ampersand key to swat through a brand’s customer service obstacles like Arnold Schwarzenegger cutting through the jungle in Predator. It may look like we’re in the golden age of immediate-results technology, but we’ve only just reached the earliest, primordial stages in its existence, and consumers have already and instantly adjusted to the instant  age.

As the world of apps grows, consumers have, and will, expect every corner of our daily lives, especially the institutions that manage our financial loans or our medical information, to be as cloud-efficient and scalable as the app that schedules an undergrad to walk your dog while you’re in a meeting. Technology tends to evolve inwardly and sensitively, finding it’s way into our banks and homes–not broadly and impersonally. Notwithstanding, loan origination systems are a deeply personal technology.

As a microcosm-example, a 2016 piece in Forbes summarized that brands who engage in direct customer service via Twitter see a 19% increase in customer satisfaction. Loan origination is one of life’s sensitive areas–vulnerable like our medical information or mortgage payments–that needs to adapt to this instant-evolution.

Picture the expectations of instant response when we have an artificially intelligent platform accessible from a contact lens. How long do you think a customer will tolerate waiting for approval or a green “success” check or a loan when all of the technology around us has already reached the speed and accessibility only dreamed up in Ray Kurzweil novels?

Waiting, whether for your breakfast sandwich or your loan decision, is as a dead and dusty a concept as your CD-RWs. Instant technology that learns for the individual and can execute in real time is the present and future. We’re already expecting banking to happen in the blink of an eye. In the future–especially in light of the cascading failures in recent financial technology–it will need to happen even faster, more efficiently, and securely.

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