NEWS
The use of artificial intelligence and machine learning continues to exponentially increase across a wide variety of industries. This CMSWire article taps several experts, including Kathy Stares, EVP, North America at Provenir, to take a look at the ways that AI and ML can enhance a brand’s data strategy and improve the customer experience.
NEWS
Gen Z, which is transitioning from school to the workforce, and has never known life without a smartphone or the Internet, has an estimated collective buying power that is nearing $150 billion. However, one study shows that only 47 percent of Gen Z — versus 75 percent of Baby Boomers and 70 percent of Millennials — has an account with a traditional bank, credit union, neobank or technology company.
In this Datatechvibe article, Kim Minor, Senior Vice President, Marketing for Provenir, discusses how alternative data and AI can help traditional financial institutions serve this unbanked/underbanked population.
Are you offering financial products to your customers? Do you want to verify identity quicker, detect fraud earlier, and make more accurate credit decisions? Introducing Provenir Data, offering simplified data access, fully maintained integrations, and one single API.
Discover how the right data at the right time can power more accurate decisions across identity, credit and fraud, covering everything from SME lending and auto financing, to BNPL, credit cards, telco, mortgages and more.
ON-DEMAND WEBINAR
Today, up to one-third of all adults globally lack any type of bank account, making it difficult to evaluate creditworthiness using traditional methods. This large population of unbanked individuals represents significant growth for innovative organizations.
A major issue hindering access to credit lies in the limited types of traditional data typically used in credit scoring and risk profiles. To make financial services available to the unbanked and underbanked population, alternative data provides new insights to support credit decisions while also detecting fraud.
How can fintechs and financial services providers begin this journey to remove barriers to financial inclusion and expand their potential audiences? By pioneering accessible data and open APIs to provide credit decisions and prevent identity fraud in fast, instantaneous actions, banks can begin to lift the unbanked out of financial exclusion.
Executive Vice President, North America,
Commissioner, UK Financial Inclusion Commission
Researcher, Finextra
ON-DEMAND WEBINAR
It’s been a few months since the FCA’s Dear CEO letter, outlining their concerns with the rising cost of living. As it predicted, inflation is impacting household budgets resulting in an increased demand for credit.
Vulnerable customers are set to be hit the hardest. More of the UK population are also predicted to display characteristics of vulnerability over the coming months.
The upcoming Consumer Duty is intended to raise the bar to address the FCAs growing concern lenders may not be doing enough. One thing for sure is that traditional data sources don’t have all the answers – lenders need to understand their customers’ real-time financial position to predict future risk and put the customer at the centre of their business.
Leaders in the credit data space, DirectID and Provenir co-host this webinar with guest pannellist Jo Pearson from NewDay discussing the changing pressures on consumers. They explore and share knowledge on the alternative data sources available across the credit life cycle that lenders need to produce better customer outcomes.
Partner Manager, DirectID
Managing Director, UK & Ireland, Provenir
Head of Customer Outcomes, NewDay
Do you struggle with your organization’s data strategy? Do you have access to the data you need to make smarter decisions? With the right technology, you can overcome any data challenge to reduce risk and grow your business.
Access any data, anywhere, on-demand through one API – read the infographic to learn how.
Data Your Way – Streamlining Your Data Strategy with Provenir Data
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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.
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.”
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:
How can you get smarter with your risk decisioning? Look for an all-in-one solution that functions like a smart home – one centralized platform that allows you to manage and control it all: data, AI models and decisioning.
Browse the infographic below for more info on the benefits of unified access to AI-powered decisioning and data.
Get the eBook and discover how a unified solution can help you change the way you think about your risk strategy.
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(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?
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:
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:
To launch with an MVP approach you’re going to need data to support three key areas:
An MVP for consumer lending could look like this:
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.
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.
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 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:
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:
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:
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:
For BNPL providers that want robust data supply chains across credit, identity, and fraud while maximizing efficiency, an optimized flow could look like this:
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 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.
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:
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.
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:
We see the world in 3D – and we should view credit risk the same way. As a lender, you’re often forced to determine credit risk using only a traditional credit score. But that leaves your perspective lacking.
Read our definitive guide and discover how using alternative data can enable you to:
Create a more agile approach to your credit decisioning process
Say yes to more customers, including those who are thin-filed or unbanked
Improve your decisioning accuracy and offer more cross-sell/upsell opportunities
Discover more about our Global Data Marketplace and supercharge your data strategy.
What is a decision engine and how does it help your business processes?
