Meet Tatiana Sotnikova 👋
Tatiana is our accomplished Insights Product Manager at Atto. With almost three decades of senior-level risk management expertise, Tatiana has held prominent roles in major commercial banks. Notably, she served as the Chief Risk Officer and Member of the Board for Piraeus Bank ICB, and her contributions extended to consulting for the World Bank.
In her insightful article, Tatiana Sotnikova, takes you on a compelling journey through her wealth of knowledge, shedding light on the evolving landscape of affordability assessments. She articulates the need for new data sources, drawing on her extensive experience to underscore the importance of informed decision-making for underwriters in the ever-changing world of lending.
Introduction
In the intricate tapestry of credit risk, affordability assessments emerge as the protagonist, scripting the decisions that shape the financial terrain. These assessments, casting a spotlight on an individual's fiscal journey, are the guiding force for lenders, determining their capacity to shoulder new financial commitments while safeguarding their financial stability. Historically, though, the traditional avenues for conducting these assessments pose a key set of challenges, not only for lenders but also for customers.
Here is a few of the key reported challenges that stop underwriters from making the best decisions:
- Backward-facing or historical data
- Unforeseen financial changes
- Partial scope of traditional credit reporting
- Static decisioning datasets
- Lack of cashflow visibility
Let me explain 👇
Life can change at any moment
Key challenges for lenders arise from the limitations of traditional affordability assessments, particularly in the face of historical data that tends to be backward-facing. The reliance on static data can result in a distorted portrayal of an applicant's current financial status, necessitating the integration of additional datasets for a more accurate assessment.
Life's unexpected events, such as recent job transitions or unforeseen medical expenses, have the potential to significantly alter a person's fiscal outlook, introducing complexities not immediately evident in conventional documents.
Credit reporting
The static nature of credit data can present some challenges. Traditionally credit assessments have grappled with a lack of visibility into an individual's cashflow dynamics. While credit reports highlight existing debts and past behaviour, sometimes they don't provide crucial details about income stability, volatility, and the ability to manage new financial commitments.
Without the insight into the financial heartbeat of an account (cashflow), it's difficult to make an accurate assessment of a person’s capacity to take on additional financial obligations.
For example, any underwriter will tell you; significant repayments related to the COVID-19 pandemic have distorted credit scorecards, demanding recalibration to reflect the current financial landscape accurately.
Buy now (hopefully) pay later
But one of the most prominent challenges we're hearing from the market is the emergence of Buy Now Pay Later (BNPL) companies. The presence of the quick-shop lending experience exacerbates the issue, as they do not currently report transactions to credit bureaus but are being widely used by the new generation of borrowers.
This is likely to change, but at the moment there is a significant blind spot for lenders, impeding their ability to assess credit risk accurately and leaving them unaware of critical financial commitments made through BNPL services.
In navigating these challenges, lenders are increasingly recognising the need for a more dynamic and comprehensive approach to affordability assessments that incorporates real-time, diverse data sources for a nuanced understanding of an individual's financial health.
Bank transactional data
Amidst this complexity, bank transactional data emerges as a technological answer, transforming our perspective on gauging an individual's affordability and enhancing the data that is currently available. It's not that bank transactional data can provide everything an underwriter needs - but it provides that extra dimension required to understand the ever-evolving circumstances of a customer.
This paradigm shift, propelled by PSD2 and actualised through open banking, revolves around the finesse of data categorisation. It provides lenders with direct permissioned access to an applicant's financial profile, offering an immediate, precise, and exhaustive understanding of their financial health through categorised bank transactions.
Seamless digital experiences
I’ll give an example of how that looks for underwriters shortly, but I wanted to touch on the benefits to the most important person in the equation – the customers. The wonders of open banking technology extend beyond efficiency; they resonate with the surging demand for personalised and streamlined products and services. By going through the open banking journey, customers can highlight their affordability in seconds, in comparison to times gone by, where this could take much longer.
By harnessing the vast troves of data at its disposal, open banking not only simplifies affordability assessments but also aligns seamlessly with the contemporary pursuit of effortless digital experiences. It also acts as a levelling force, providing those with limited credit histories the opportunity to highlight their creditworthiness through alternative data.
Atto: Getting to the bottom of affordability
Now we’re here. I’ve been working with Atto for the last 2 years now, so I wanted to talk about how we approach affordability analytics.
Renowned for delivering credit risk insights grounded in bank transactions, we obsess over how financial institutions can make swift and accurate decisions regarding customer affordability - with the goal to streamline the conventional loan application process for all.
What truly sets us apart is the ability to transcend ordinary factors like income and expenses. We delve deeper, providing insights into discretionary spending and potential future scenarios, such as job loss or health setbacks. These critical supplementary details assist financial institutions in gauging whether a new loan could potentially jeopardise the applicant's financial stability, adhering to the principles of responsible lending.
I was asked in my days as a Chief Risk Officer: when will transactional data become crucial? The answer is – two years ago. With the economic strain applied by the pandemic, the sharp rise of BNPL and the move towards gig-economy and freelance earning, the data of yesterday simply is not enough.
It's also worth pointing out (at risk of stating the obvious) that lenders are always looking for a competitive edge. With bank transactional data, lenders can start to monitor their portfolios through a real-time lens, highlighting customers who have improved their affordability and therefore may be interested in additional products.
What the underwriters need
The core element in assessing someone’s likelihood to repay a loan is access to income information. Transactional data offers a comprehensive view of income, its stability, volatility, expenses (fixed and variable), and monthly saving accumulation. Combining these insights with credit repayment information offers a 360-degree view of the customer’s profile.
The affordability analytics we provide focus on attributes related to income, but also expense categorisation and previous credit commitments, to help assess the likelihood to repay. What we are doing here is advocating for cashflow-based underwriting as an alternative or additive to traditional consumer underwriting based on credit bureau scores – so that lenders have all the data they need to make the best, fairest decisions for their end-customers.
Challenge or opportunity?
While we are seeing the industry move towards alternative datasets to bolster what is already available, the process will take time – and it will not come without its challenges. Not everyone is willing to connect their primary bank account, often opting for secondary or tertiary accounts. That is why it is important that we apply the process to the correct use cases. We have seen on our platform, if we can nail the use case and the customer experience, we can see conversion rates of up to 90%.
Proper categorisation and standardisation of data are also crucial for effective cashflow-based automated underwriting. The challenges are not just mathematical but in how the industry engages with these tools. Wise and careful implementation, learning through champion-challenger approaches, and reconsidering credit decision trees are essential.
Wrap
I've been on the other side of the table as a Risk Officer, and I’ll admit, we tend to be risk averse by nature. But right now, the biggest risk is what we can't see. I believe, just like most lenders do, that there's additional data out there that can help us understand our customers better and improve how our portfolios perform.
As things are changing, I think bank transactional data, along with the analytics we use, is becoming one of the most useful tools for lenders to understand today's borrowers. Our analytics are not just about making things more efficient; they're about really getting to the core of each customer's finances at every step of the credit risk lifecycle; from pre-sell and loan origination, through portfolio monitoring to collections and recoveries.
The goal here is to make the right decisions - that's what every underwriter wants.
And with the addition of bank transaction data, they can do just that; without ever compromising on the targeted credit risk.
