Does Data-first Lending End with Credit Monitoring Systems?

Take a peek at what's next for Banking as credit monitoring and borrower surveillance intensifies

Indian public sector banks collectively owed approximately 6.17 trillion rupees in non-performing assets in FY 2021. This value was much higher, at around 7.5 trillion rupees in FY 2019, indicating a slow but slight relief for India's economy in terms of non-paying assets.

Surprisingly, the pandemic is being recognized to have eased this situation with its demand for quick digitalization. An important outcome – Data-first Banking – is helping lenders overcome difficulties in borrower account monitoring and recovery strategies.

Data-intensive systems help Financial Entities build end-to-end processes, improve borrower relationships, and above all, get accurate risk assessments using massive volumes of data. Here, Credit Monitoring Systems are driving this transformation.

A Step Closer to More Accurate Loan Default Predictions

Where Banks face increasing pressure from regulatory bodies for cleaner data, predictive analytics is unlocking its true potential. To enable predictions of loan defaults, Early Warning Systems (EWS) use financial and non-financial data from internal and external sources.

Where Credit Monitoring Systems (CMS) mainly monitor transactional data, Early Warning Systems (EWS) is for advanced analytics, offering analysis for alternative and ESG data too.

For comprehensive credit monitoring, new age systems without Early Warning capabilities upgrade Banks' infrastructure to accommodate advanced analytics in the future. Here, credit monitoring systems solve a common problem — unintegrated data.

Where data is scattered because of unintegrated systems a Credit Monitoring System plays a key role. Aiding in the unification of data from disparate sources, the deployment of this system initiates transformations for data integration, real-time data updates, and simplified information sharing. Mainly, it helps Banks build Centralized Data Repositories (CDRs).

Let’s look at how CDRs help and what else is important to create a data-intensive, comprehensive credit monitoring ecosystem.

Solution Architecture for Seamless Credit Monitoring

A Credit Monitoring System (CMS) streamlines data from underwriting processes, transaction systems, and third-party service providers like credit card companies and more. The system compiles huge amounts of data, checks, and creates reports for covenants, automates periodic reviews, and offers analytics for statements (Stock statements, Financials, etc.).

To bring simplicity to the process, a question solution architects ask today is – can the complexity be managed better?

Future-ready, Modernized Core Banking Systems

Modernization of Core Banking Systems (CBS) begins with building Centralized Data Repositories (CDRs) which can be accessed by all teams. The next step is to integrate data from all sources and build data pipelines for simplified data flow to various teams like collection agencies, borrower account managers, and business analysts.

Where NBFCs are increasingly competing in the lending space, modernized systems should help Banks innovate faster. For example, cloud-enabled, web-based Credit Monitoring Systems, today help Bank managers reallocate funds through mobile apps too.

Streamlined Reporting and Insight Generation

Apart from driving the development of Centralized Data Repositories (CDRs), Credit Monitoring Systems open the gates for better regulatory compliance. Borrower tracking and reporting for a variety of financial metrics, including revenues, cash flows, and leverage levels can be easily automated to meet regulatory mandates.

With the same data, bankers can analyze financial statements and shorten timelines for credit write-ups. Using Credit Monitoring Systems, calculations and statements are accurate with limited manual intervention.

RPA and AI/ML for Intelligent Automation

AI/ML-powered automation boosts the efficiency of the credit monitoring processes. Apart from making processes cost-effective, it helps Banks and Financial Entities reduce manual intervention and repetitive data checks. This simplifies data processes at very large scales.

CMS equips Banking teams with AI/ML-powered automation, also known as intelligent automation (IA), and integrates it with application program interfaces (APIs). These systems pull information from borrowers’ accounting software, for any number of accounts.

Also, optical character recognition (OCR), an AI-based function, helps convert financial statements and accompanying notes from scanned documents or non-readable PDFs. This saves time for these previously manually-operated tasks.

What’s Better than a Credit Monitoring Solution?

Early Warning System (EWS) is an effective monitoring solution for loan portfolios, prescribed by RBI back in 2015 to lower loan-loss contingency. The system puts in place a proactive credit monitoring practice using predictive analytics. Research shows the system helps FIs maintain a strong risk appetite, a higher return on equity, and a better capital yield.

The warning signs of default-prone borrowers, monitored by EWS can be grouped into five areas: transactional, financial, non-financial, external, and statistical. The system sources both structured and unstructured data from multiple sources for accurate predictions.