Financial Technology (FinTech) developments have allowed firms to analyse large amounts of data like never before. According to research firm Gartner, by end 2024, 75% of firms will shift from piloting to deploying non-traditional technologies, triggering a five-fold increase in streaming data and analytics infrastructure.
FinTechs use the power of Data Science to quickly assess creditworthiness and help firms make credit decisions, therefore revamping traditional credit underwriting. FinTech developments have drastically changed the role that credit underwriters undertake – they now need to upgrade their mindset, skillset, depth of understanding and expect a shift in work segregation to stay up to date with the ever changing landscape of credit underwriting.
Traditional credit underwriting is in need of a revamp.
The job of credit underwriters, simply put, is to evaluate a person or a firm’s ability to repay loans. In other words, underwriters verify and assess the risks lenders need to take when giving loans. This means that a variety of assessments needs to be done to credit history, income, assets, loan size, and more to set a fair borrowing rate.
Traditionally, credit analysts go through a long process to assess creditworthiness. They need to analyse financial reports and gather information to generate financial ratios and conduct risk analysis. They also have to complete long loan application forms, which are then subject to lengthy approvals by a loan committee.
Since assessing creditworthiness is a manual underwriting process, it can take weeks or months for banks to relay the credit decision to the borrower. To make matters worse, borrowers may be denied credit due to a thin profile where they do not have enough data with the credit bureau to facilitate any assessment of their creditworthiness. While the problem of a thin profile may be difficult for countries such as Singapore to imagine, it is common in countries where financial exclusion prevails and where a fraction of the unbanked or underbanked citizens are poorly served by financial institutions. These citizens may not have any bank accounts or an established credit history. Traditional credit underwriting is also unable to cope with high loan counts, or small loans with a short tenor. It is hence unsurprising that FinTech has stepped in to resolve these problems.
FinTech is here to stay in a post-COVID world.
Digital technologies have proven to help digital lenders create breakthroughs in terms of both the quality and quantity of loans processed. These lenders will change their underwriting and monitoring practices to further improve credit decisioning, stress testing, and operational flexibility. Real-time data aggregation and analysis as well as predictive analytics are likely to mature over the next few years.
All these advancements also signal the increase in collaborations and partnerships between banks and FinTechs, e-commerce platforms and payment gateways (in the form of Embedded Financing), and supply chain platforms and FinTechs (in the form of Lending-as-a-service). In these cases, partners are likely to front the relationship and provide additional services to end-users, while FinTechs provide the financing while taking credit risks.
The proliferation of FinTech advancements is not to be dismissed by credit underwriters. This is particularly so since FinTech promotes a contactless and highly efficient digital lending solution, making it resilient as the pandemic turns endemic. Credit underwriters will need to upskill and keep themselves apprised of FinTech developments to stay relevant.
FinTech developments have changed the scope of credit underwriters.
With the recent development in FinTech partnerships and FinTech advancements, the commercial and consumer counterparts have experienced a new model of underwriting while the corporate segment has remained largely the same. Credit underwriters now need to embrace alternative credit scoring mechanisms, build strong data-related capabilities, foster a deep knowledge of credit risk and partner operations, as well as ascend up the traditional credit role ladder.
- #1. Change in mindset – Embrace alternative credit scoring mechanisms
Banks and financial institutions have a very clear pre-defined level of acceptable risks and a clear idea of what type of borrowers they are looking for. However, with FinTechs aiming to foster financial inclusion, there needs to be a mindset shift. Credit underwriters need to be willing and ready to explore and provide loans to persons and organisations with thin profiles.
This means being comfortable with different assessment types that are not your traditional financial reports or credit bureau reports. Such alternate credit scoring mechanisms assess a borrower’s digital footprints to determine their creditworthiness, and can include data collected from electricity and telephone providers, and also financial services like insurance or mutual fund players. Some FinTechs even trace digital footprints on social media to find out more about the characteristics, lifestyle, and behaviours of a borrower. Other FinTech firms also use psychometric tests to evaluate their borrowers’ ability to repay.
- #2. Change in skillset – Build strong data-related capabilities
Given the increase of collaboration and the emphasis of data, credit underwriters must be comfortable working with data. Since they will be liaising closely with the data science or model validation team, there is a need to not just grasp statistical methods, but also execute simple data exploration or extraction.
Take for example, the social media credit scoring algorithm which creates a Yelp-style review of individuals. Without data collection and analytical skills, the credit underwriter will be unable to make sense of the large amounts of qualitative data points across social media networks – data points that indicate an organisation or individual’s willingness and capacity to repay loans. However, having data evaluation skills allows the credit underwriter to monitor and forecast spending patterns, as well as validate education and employment history through location check-ins, posts and tweets. A good credit underwriter who can comprehend all this information may even go on to evaluate an organisation or individual’s revenue or income levels.
- #3. Change in depth of understanding – Foster a deep knowledge of credit risk and partner operations
A surface understanding of various types of credit risks is no longer sufficient as traditional underwriting documentation alone is inadequate for FinTech loans of tomorrow.
Credit underwriters will find themselves disadvantaged if they lack a deep understanding of the different types of credit risks and data provided by FinTech partners. Without a strong foundation, credit underwriters cannot map the various data points to different credit risks.
- #4. Change in work segregation – Ascend up the traditional credit role ladder
The world of credit underwriting no longer accepts a one size fits all model. The number of products and sub-products are too large for non-customised solutions to be effective. Hence, the need for automation to streamline processes is more crucial now than ever.
This means that the segregation of work between credit teams will likely move from vertical segregation (junior, mid and senior, analyst and approver) to horizontal segregation (each credit to be in charge of a few products). A strong understanding of portfolio credit risk management will become increasingly important for credit underwriters as they take on more responsibilities in their products during an earlier part of their career.
Credit underwriters play a critical role in making FinTechs sustainable.
As FinTechs strive towards financial inclusion, they will work and experiment with new assessment methods to better evaluate persons or firms with thin profiles. As such, in order to thrive, credit underwriters need to be uncomfortable with uncertainty and change.
One thing is for certain though – Both credit and data skill sets will be indispensable in the near future. Some years from now, there may no longer be Credit Analysts or Credit Managers, but Credit Data Analysts and Credit Data Scientists/Managers.