Credit risk analysis is vital in any financial organization that advances credit facilities to its customers. But lenders have been faced with uncertain times that call for new approaches to credit risk, supporting digital lending.
Consequences of the pandemic, geopolitical tensions, rising inflation, and interest rate hikes will likely increase defaults, especially after government cushion strategies subside.
In a volatile credit market, lenders need to make decisive, timely, and informed decisions to manage their credit exposures.
Some lenders have changed their risk appetite, aligning it with internal policy changes. For example, we have seen an increase in lending to SMEs as well as to the underemployed as a move toward the revival of the highly-affected micro businesses.
Post Covid, more and more banks are embracing digital lending to fill in the portfolio gap and expand their market. What’s more, digital credit does not only cover short-term mobile loans now but long-term credit as well.
To support these new strategies, lenders are accelerating the digitization of their credit process to facilitate auto-decisioning for loan applications. There is an increase in the use of Machine Learning, Analytics and Artificial Intelligence by banks in the credit process.
In fact, credit digitization has been a key area of focus for lending organizations after the pandemic, driven by the need for banks to move from the traditional way of serving customers in branches to an omni-channel digital environment.
When it comes to loan decisioning, credit institutions need to pay attention to a number of key imperatives in order to stay competitive in the current uncertain reality:
1. Robust IT infrastructure.
To support the increased volume of loan requests originating from multiple digital lending and other channels, credit institutions need to have a robust and seamlessly integrated IT infrastructure.
2. Borrower segmentation.
In order to embed the correct algorithms in the lending engine and reduce the non-performing portfolio, lenders should do borrower segmentation based on risks identified through data analysis.
By doing so, lenders save time and money by getting to the heart of what matters to specific groups of consumers.
The segmentation factors could include demographics, behavior, transaction history, needs, opinions, interests, and more.
3. Borrower engagement at first signs of distress.
A further tactic is to have a strategy in place to engage with borrowers as soon as they show signs of distress and group borrowers with similar characteristics to resolve them in a comparable way. That is, have a loan collection system that triggers an exception whenever a loan goes into arrears.
4. Agile credit workflows for digital lending.
Have procedures that are clear, concise, and fit-for-purpose - but also be able to respond to changing conditions. Define simple, configurable credit workflows that ensure that the business adheres to the laid-down credit policies. Digitize credit workflows end-to-end to reduce turnaround times for decisions and to minimize costs.
5. AI, machine learning, and automated credit scoring & decisioning.
Adopt AI and Machine Learning models that can continuously analyze historical data to help you reach better future decisions. Use Artificial Intelligence to come up with the appropriate algorithms to score borrowers, contain credit risk & ensure compliance.
With AI it takes a few seconds to score a customer and approve a digital loan!
It is also key for credit teams to have the tools to do more vigorous reviews and analysis of the non-performing portfolio.
6. Bonus tip: Reassessment of data and risk rating models regularly.
Understand what data and data sources are required to make credit decisions. The sources can include Credit Reference Bureaus, historical loan information, e-wallet transaction history, telco data, psychometrics, open banking data, social media, etc.
In addition - have regular reviews and backtesting of rating models to keep up with the changing market dynamics.
At Software Group and SG NewTech, we recognize the needs of banks in their ongoing digitization & automation of the credit process. To meet them, we’re continually improving our credit origination solution, CreditQuest, the world-class credit risk management system, which seamlessly integrates to lenders’ existing digital channels and any decision engine, to facilitate faster decisioning of loans and overall portfolio health.
