The Financial Data Evolution: Unraveling lending in open finance with Akoya’s Rishi Kapadia
- As part of Tearsheet Talks: Lending x Credit, Data, Rishi Kapadia, product manager at Akoya, discussed "The Financial Data Evolution: Unraveling Lending in Open Finance."
- Kapadia sees open finance facilitating an evolution in the whole user experience in the lending process.
There’s been a rise of data aggregation in PFM use-cases -- where's this coming from? We're also seeing more adoption of lending-related use cases, as the market shifts for the use of financial data aggregation in this space. Open finance is facilitating an evolution in the whole user experience in the lending process.
As part of Tearsheet Talks: Lending x Credit, Data, Rishi Kapadia, product manager at Akoya, discussed "The Financial Data Evolution: Unraveling Lending in Open Finance."
Rishi, a UK native who relocated to the US five years ago, has nurtured a career predominantly centered around consulting in the financial services sector. While delving into the lending domain, he later shifted his focus to embrace the dynamic world of open banking. In his leisure, he finds joy in watching cricket, hitting the slopes for skiing, and rekindling his passion for golf.
The rise of PFM powered by data aggregation
Rishi Kapadia, Akoya: Yeah, let's start with PFM. So personal financial management use cases have been one of the main drivers in financial data access adoption in recent years. PFM apps allow users to track their spending, set budgets, manage their finances. And these apps typically use financial data access to provide users with a comprehensive view of the financial situation. The data behind the these apps tends to be the the transaction data of a customer.
So you're looking at a checking account or savings account. Sometimes you're looking to liability accounts, which show loans line of credit and such. But essentially looking at the transactions and giving you a holistic view to the customer on their financial financial situation.
Beyond PFM, I think it's also worth knowing there are other areas where data access has increased significantly in its usage and continues to make large strides in and those are like streamlining the account opening funding and payment processes. So just to give you an example, picture this: in the past, opening and funding an account required submitting numerous pieces of documentation. So if I'm trying to open a checking account, I might have to submit my identity and address verification documents, along with setting up that manual transfer to fund that account. So this in turn is very cumbersome and time consuming. And often, it deterred people from adopting new and existing financial solutions or migrating over to existing financial solutions.
And so thanks to open finance, we're seeing this evolution of data access APIs drastically changing that landscape. And this essentially just helps build and increase those seamless and efficient consumer experiences. Now, users enjoy this hassle free onboarding experience without the need of this extensive paperwork and manual transfers. They can get a full view of their financial history and situation at a click of a button. And so if you really think about it, the positive impact is twofold. One, it empowers financial institutions to provide better services and drive the adoption of diverse fintech offerings. Secondly, we see it offers consumers the convenience they crave. And it's only builds the trust and loyalty towards these emerging financial solutions.
Market shift of data underpinning lending
In recent years, there's been a growing shift towards using these lending use cases and there's a number of factors. Let's go into the first one. More FIs are providing access to this consumer data. And this is being aided by core processors, providing access to the long tail accounts in the market. This is truly due to the growth of open finance, which allows users to share their financial data with third party applications like fintech apps.
A great example of more access to consumer data is Akoya. We initially started with just the large US banks and started to gain data coverage among those. And now we've integrated and continue to integrate with more core providers into the mix. That really captures the long tail coverage that folks need because often they're working with one big bank and maybe some local credit unions or local banks. You'll see greater confidence from these financial services organizations in adopting these aggregation services. One of the things to add here is Akoya's direct connections provide a reliable and secure connection. And such advancements really propel that confidence in lenders to opting to use this financial data.
The second point I want to make is: we're seeing a shift towards lenders leveraging open finance as they're under increasing pressure to make accurate lending decisions. And this is due to two things. One is there's increasing competition in the form of digital lenders and traditional lenders and all are innovating. You have BNPL, digital personal loans, traditional personal loan lenders, and digital mortgage lenders, and some of the digital mortgage lenders have taken a large market share today. The second reason is the need to reduce the risk of lending to borrowers who are likely to default on a loan. Especially with these tightening economic conditions, we're seeing additional data helps to supplement those underwriting models that they have in place today.
And then, finally, financial data access can be used to develop new lending products and services. Lenders often tailor products to the specific needs of borrowers, for example, thin credit file, as we've seen, or those who have been excluded from traditional lending markets. This has been made possible by looking through detailed transaction history of a borrower and enabling cash flow underwriting models in the market.
Benefits of using OAuth connections for lending data
OAuth connections are really a secure way for lenders to connect to financial institutions to access customers' data. At Akoya, we have 100%, OAuth connections to financial institutions and in turn, you gain unmatched data reliability and accuracy. Essentially, all connections use tokens to authenticate users and grant them access to those data APIs. So you do this without ever storing or sharing the customer's user log in. This essentially helps protect the privacy of a consumer's financial data.
OAuth connections and API connections are generally easy to use. Most of the time out the box, lenders can start to integrate with these APIs very quickly. And they're often very developer friendly. At Akoya, we aim to be as developer friendly as we can be. It gives lenders easy access to get started with financial data access and start to make those informed lending decisions. And then finally, direct to bank API connections are another part where Akoya has been focused on -- it essentially ensures reliability in the data access because you're establishing a direct and secure connection between the data access network and the user's bank. You're bypassing any intermediary or third party services, minimizing the risk of data discrepancies, delays, or potential data breaches, which you might find with methods like screen scraping.
Cash flow underwriting?
Historically, lenders were limited to using traditional credit scores and other credit reporting agency data points to assess a borrower's credit worthiness. I think as open finance evolves, given the access to more data, lenders now consider a borrower's entire financial picture and includes income and expenses. So cash flow underwriting is definitely a great example of this where I'd say the income and expenses are analyzed to determine the credit worthiness of customer often with a thin credit file. Cash flow underwriting is still in its early stages, being adopted by the fintech lenders, using that data to serve a broader segment of borrowers with a differentiated offering. But with the larger FIs, we're seeing slower adoption for these cash flow underwriting models to supplement or replace the existing decision models in place. I don't think cash flow underwriting is the most common lending use case, but it is definitely a growing segment we're seeing and keeping an eye on.
In recent times, in the lending industry, we see a growing reliance on financial data APIs during the customer onboarding process. So this shift aims to authenticate a customer's income and assets during the onboarding process, eliminating the need for those conventional methods such as uploading bank statements and other documents. This transformation streamlines the loan approval process, making it more efficient and user friendly for customers.
Let me give you two examples. One is in the context of a personal loan application. One of the pivotal steps as part of the loan application is verifying a customer's income. If you were to submit a personal loan application, you are required to input your income information. And as you input the income information, that input is usually compared against the data points provided to a lender by its conventional lending data. That data usually comes from a credit reporting agency, and that figure should align with what has been inputed by the customer. There usually aren't any further inquiries on the income. That's a positive journey customer journey.
On the flip side, if we look at where there is a disparity with the declared income and what the lending data posted, the customer is usually asked to furnish some supplementary documentation. And this documentation could be in the form of bank statements where they then look through the bank statements to identify the income or it could be tax payroll statements to see what income has been for the past six months, 12 months, and such.
However, with financial data access, you see it being used as a facilitator in this, so that the mechanism essentially empowers the user to establish that secure connection with their respective banks. This connection, in turn, empowers the vendor to extract the transaction records, which are intricately linked with the customer's income, and then they automatically calculate on the back end the stated income and whether the actual income maps up for that customer. That's one great use case we've seen, especially on the onboarding process.
I would also call out the mortgage application process -- when you're applying for a mortgage, you have to validate that you have enough money for the initial payment, which is the down payment. This could be 5%, 10%, or 20% depending on the loan type you are asking for. Before, this was done by looking at statements, but now the market has products, which are called, as is reports, that basically summarize your customers financial details, including how much money is in their account on a specific date and the transaction history. So the underwriter is really able to validate this information and confirm that there are sufficient funds in the account to process this loan for approval. This makes the mortgage application process smoother and clearer for everyone involved.
Beyond onboarding, there are some other areas where lenders are using financial data access. Let me touch upon a few: credit scoring is definitely one of them, where they use the data to develop their own credit scoring models. Again, this comes back to the accuracy point and tailoring underwriting for those specific products that they are building for. The other one is fraud detection, which we see a lot in the market, and lenders using that data to just identify fraudulent transactions. This protects both the lender and borrower from financial losses.
The most interesting one, I think, is risk management. And we haven't seen a lot of this, but I think we'll start to see a lot more of this. Lenders are able to continuously monitor the borrower's financial behaviors over the long term. So if there are signs of financial instability or risk, lenders can take those appropriate actions and adjust the terms of the loan or maybe offer financial guidance to their customers. I think this is a really good use case, because it really tailors the services for the customer. So being more customer centric and essentially holding on to that customer for a longer period of time and maybe being able to upsell at some some other products to them.
Open finance and lending
Open finance provides borrowers with more control over their data. Borrowers can choose which providers they share data with, and they can always revoke that access at any given time. Internally, it creates an additional level of trust for those consumers. At this point, they know sensitive documentation is being transmitted through a safe and secure ecosystem, rather than the paper copies we might have seen, historically, many years ago. And the email scams we've seen more recently. This gives the customers peace of mind knowing that they're in control of what data is shared, and with whom and for how long.
Additionally, I think the integration of financial data APIs is a game changer for both digital and traditional lenders. I think this point often doesn't get considered enough with lenders. But I think when you're in the lending space, conversion rates are huge to you. And through the use of streamlining the verification process for, let's say, income and also the mortgage application where we said asset reports help them identify the downpayment, it reduces the friction associated with these traditional documents, submissions, and customers are more inclined to complete the loan application rather than stall halfway through the application. So lenders are really seeing higher or successful conversion rates. And that leads to increased business growth, but also customer satisfaction.
Financial data APIs are improving the end to end flow of a customer's journey: from the application stages to getting the approval to actually servicing the loan and paying off that loan through disbursements. It's improving every layer of that journey.
Outlook on lending and open finance
Navigating the lending landscape, especially in the context of open finance and data integration, presents significant challenges. Look, the lending industry is under stringent regulations. And then there's emphasizing the necessity for compliance. Particularly, when you're automating processes like income verification, it's evident that the demand for direct to bank OAuth connections will surge among lenders. Those who intend to harness the capabilities of financial data access services, they'll look for high uptime and dependable connections.
And in addition to that, I think there is the importance to source directly from the banks. This shift aims to mitigate some of the risks associated with the inaccuracies of data capture through methods like screen scraping, for example. I think financial institutions are really going to transition towards more direct sourcing, which kind of aligns with the enhanced accuracy and reliability that they need to run their underwriting models.
Then finally, this evolution is not limited to a singular purpose. A broader spectrum of use cases is emerging through the entire journey. This speaks to the industry's intent to streamline and fortify every step of the process, ensuring efficiency and transparency for customers, and ultimately, a seamless experience for both lenders and borrowers. We're working towards prioritizing the build of specific solutions, those that the customers wants and needs the most -- solutions that save the customer and lender valuable time, effort, and also money.