Opinion

Enabling better credit decisions using AI and automation

Meghna Suryakumar | Updated on May 10, 2021

Agile software platforms equip business leaders to standardise decision models with clear business logic, reducing the possibility of human error and speeding up the process.

Technologies such as artificial intelligence and machine learning models that were once considered nascent and on the horizon are now at the forefront of financial services, especially lending. Hesitation to adopt these technologies could prove costly for lending institutions because of the game-changing advantages they bring to the business model. Here’s a quick dive into the latest in lending technology and business to know more.

Compared to the global landscape, India’s financial services sector has thrived in the past decade. Much of this growth is owed to the rapid digitization of data and data-related products and services. India’s digital thrust was made possible by various public and private collaborative initiatives, many of which were led by India’s government and industry bodies. The resultant outcome has shifted India many places forward according to the global digital competitiveness index and a McKinsey report. Over 80 per cent of individuals now own a bank account, in contrast to just about 20 per cent a decade ago. According to the World Bank, the financial inclusion of unbanked businesses and people has more than doubled in India over 2010-17.

While there has been much headway in financial inclusion, this progress took place on a small base. There remains a greater set of businesses outside the formal credit ecosystem, for whom access to capital remains the biggest hurdle to economic progress.

Economic growth slowed down in recent years, and it received a massive jolt owing to the ongoing Covid19 pandemic. As production sputters along, banks and financial institutions that have fed liquidity into the system up to this point need to find new avenues to sustain healthy growth.

Lending as we knew it

Loan lifecycle management is resource-intensive and can take long periods. These processes also incur significant manpower costs because of the many steps involved - from the initial phase of screening prospects to making the lending decision, managing underwriting and disbursals, monitoring the portfolio, and finally, collections.

New-age technologies coupled with alternative data sources make it possible to automate a wide variety of tasks and processes which require high operational bandwidth. For every step of the way, there is a more efficient and reliable alternative using digital automation aided by AI/ML tools. The advantage is not just in cost reduction but also in terms of process efficiency and customer experience.

Seamless onboarding and digital trust

Today sophisticated tools allow lenders to digitally onboard customers by aggregating applications and auto-populating authenticated data into forms from multiple channels. These customer profiles are put through an initial decision-making model with the requisite checks and balances to provide sound credit decisions. This streamlines the screening process and can also help identify the probable bad apples without bias. For example, one suitable customer has placed a recommendation engine filters and matches prospects with different financial products.

And finally, the task of collecting a customer’s residual documents is done by getting them to directly upload these to the platform. So overall, customer onboarding can now be done safely and remotely in a matter of minutes (sometimes as low as 90 seconds) as opposed to weeks of running around and bureaucratic paperwork.

Risk assessment and underwriting via automation

Credit scoring algorithms can be tailored specifically for financial institutions based on their domain expertise, size of the loan book and risk appetite. This algorithm can also be enriched by integrating alternative data points about borrowers, such as employee data (from EPFO), legal dispute data (from courts databases) and news sentiment analysis. ML-driven risk scorecards are also used to evaluate groups of connected promoters or directors and broader industry parameters such as regulation or consolidation. The workflows of underwriting, including the analysis of collaterals and assets, can also be automated. Lenders can also switch to cash flow-based lending, based on recent business performance, business outlook and predictive intelligence. This has proven to be better for some lenders when reducing their NPAs and loan defaults.

Monitoring and collections with explainable AI/ML models

One of the most overlooked aspects of the credit management lifecycle is monitoring. With the advent of AI/ML technology, data gathering and analysis can be performed at a much greater pace – sometimes in near real-time – compared to the conventional process. AI algorithms can be configured to monitor the financial and non-financial parameters of all borrowers in the portfolio. These systems raise early warnings when the possibility of risk has aggravated due to tangible inputs.

This gives financial institutions opportunities to course correct. For example, risk officers or analysts can trigger a request to their collections department to resolve a problematic situation with a borrower before it’s too late. In addition, they could proactively engage with a borrower (to either close a loan or restructure terms), sell the loans outside their risk appetite, and understand how risk in one business or industry could have larger implications for their portfolios.

The latest AI/ML tools also give lenders the power to automate various tools in their collections modules. This saves time, resources and enables lenders to focus more on the accounts that need an emphasis on foreclosure, debt restructuring, or an early write off if required.

The future of lending is here

Agile software platforms equip business leaders with the ability to standardise these decision models with clear business logic in the form of rules. These models are augmented with machine learning capabilities to enhance decision making by continually learning through sustained data being fed to the system. They reduce the possibility of human error (or bias) and speed up the process.

Automating credit decisioning and risk intelligence with explainable AI and ML tools is inevitable. It improves process efficiency and the customer experience, reduces turnaround time, provides better compliance, reduces operating costs, and enables financial institutions to be proactive. The long chain of cumbersome paper work and individual-dependent decisioning can be avoided. These tools also make it easier for lenders to move to flow-based lending rather than persisting with asset-based lending models.

Meghna Suryakumar is the Founder and CEO of Crediwatch

Published on May 10, 2021

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