How an NBFC Cut Credit Defaults by 34% with Risk Plus

Executive Summary

CapitalBridge Finance is an NBFC with a medium-scale business in East and North India. It is particularly strong in Tier-2 and Tier-3 cities. It specializes in the area of giving loans to MSMEs, unsecured business loans, and financial products. Due to the increasing volume of loans and the processing of numerous applications every month their daily operations started getting hampered.

Customer Snapshot

It runs a portfolio worth ₹850-1100 crore on MSME working capital financing, business lending, and retail financial services. At close to 18,000-22,000 applications per month, their credit operations had been growing in volume. They have a dedicated credit and risk operations department made up of over 65 individuals. They have an overall employee strength of 700-900.

They were using spreadsheets and manual ways of reviewing borrower data. But when the number of loan applications increased, setting loan terms and approving credit facilities became inconsistent and difficult. They needed a tool that would help them streamline and standardize their credit operations without compromising on their growth momentum.

The Challenge

CapitalBridge Finance was growing fast — hundreds of loan applications a week across small business and personal finance. But speed came at a cost. Their credit default rate had crept to nearly 11%, well above the industry average of 7%.

The problem wasn't less data.The company was unable to act on the data they got. The credit officers were analyzing various fragmented data like bank statements, payment behavior, transactions, bureau scores, inputs from branch offices. They kept dealing with manually created financial records. Most of the analysis was done in spreadsheets where various calculations and comparisons were made based on experience and not just data itself.

With more and more applications, processes became slow and the results were not detailed enough to act on. Credit officers spent a lot of time collecting and verifying information rather than analyzing the actual risk profile of a client. Even such things like pricing and cut-off points were being made based on general client categories.

What They Needed

CapitalBridge did not suffer from a lack of data; it suffered from lack of decision-making processes. As volume increased, it was increasingly difficult for the company to convert its information into effective decisions. In that situation, what they needed was to dive in and go beyond simple scoring systems. All of these to figure out how to make credit decisions aligned with their lending goals. At the same time, they needed to find ways to predict risks early so they could deal with them effectively.

Why Risk Plus

CapitalBridge needed a tool that didn't just score borrowers but helped them understand each one deeply, price loans intelligently, and see the future of their book clearly. They also needed to understand how they wanted to lend, not a one-size-fits-all model.

Risk Plus was selected because it allowed the company to:

Combine multiple borrower signals into unified risk views
Configure lending thresholds by borrower segment
Monitor portfolio behavior continuously rather than only at origination
Introduce explainable pricing recommendations instead of fixed category-based pricing

The deployment was completed in under three weeks, with integration into existing underwriting and portfolio monitoring workflows.

What Changed

Dynamic Pricing Recommendations

The platform introduced risk-adjusted pricing recommendations based on borrower behavior rather than static customer categories.

Each recommendation included:

underlying risk drivers
repayment confidence indicators
portfolio-level segment comparison
projected stress sensitivity

This helped underwriting teams price stronger borrowers more competitively while tightening exposure to deteriorating segments. Loan pricing decisions became more consistent across branches and escalation layers.

2. Unified Borrower Risk Assessment

Risk Plus consolidated borrower-level inputs from:

bureau records
repayment behaviour
transaction activity
bank statement analysis
branch assessments
historical portfolio performance.
Instead of manually reconciling multiple sources across spreadsheets, credit teams began reviewing unified borrower risk profiles through a single operational dashboard. This significantly reduced manual verification effort and improved consistency across underwriting teams.

3. Lending on your own terms

CapitalBridge configured Risk Plus to a balanced lending stance — calibrating both risk thresholds and pricing to sit between aggressive growth and conservative caution. If they'd wanted to chase volume, an aggressive stance would have loosened both. If they'd needed to tighten the book, a safe stance would have done the opposite. They could also go deeper, customising individual pricing rules and approval thresholds by borrower segment.

4. Faster Credit Operations

Approval workflows became significantly more structured.

Instead of multiple rounds of spreadsheet reviews and manual escalation, credit managers could review:

borrower risk summaries
pricing recommendations
projected repayment confidence
segment-level benchmarks

This reduced approval delays and improved underwriting throughput during high-volume lending periods.

5. Early Warning & Portfolio Monitoring

One of the biggest operational changes came after loan disbursal. Previously, portfolio monitoring was largely reactive. Stress was often identified only after repayment delays became visible at collection stage. Risk Plus introduced continuous behavioural monitoring using indicators such as:

delayed repayment patterns
declining transaction activity
cashflow stress indicators
utilisation pattern changes

Risk teams began receiving early-warning alerts before accounts moved toward serious delinquency. This allowed collections and restructuring teams to intervene earlier in higher-risk accounts rather than waiting for formal NPA movement.

The Results

Within six months of deployment, measurable operational improvements were visible across underwriting, pricing, and portfolio monitoring functions.

Risk & Portfolio Outcomes

34% reduction in credit defaults versus prior comparable period
NPA ratio reduced from 11% to 7.3%
Estimated ₹2.8 Cr reduction in potential write-offs through earlier intervention

Operational Outcomes

40% reduction in credit processing time
Significant reduction in manual underwriting review effort
Faster escalation handling across branch and risk teams
Improved consistency in approval and pricing decisions

Lending Efficiency Outcomes

28% improvement in loan pricing accuracy
Better alignment between borrower quality and lending terms
Improved visibility into emerging portfolio stress segments

"Risk Plus didn't change how we think about credit, it gave us the tools to finally act on what we already knew. We're not reacting to defaults anymore. We're preventing them." — Chief Risk Officer, CapitalBridge Finance

Before & After: Evolution of the Decision Process

Prior to Risk Plus, the decision-making process involved going from data to spreadsheets to judgment calls that were inconsistent at times.

However, the adoption of Risk Plus has streamlined the process such that data leads directly to insightful risk assessments and pricing advice.

Why This Matters for Other NBFCs

CapitalBridge’s situation is not unusual. Many mid-sized NBFCs across India already have access to large volumes of borrower data through LOS systems, bureau integrations, transaction histories, and repayment records. The challenge is usually not data availability; it is decision-making at scale. As lending operations grow, issues like inconsistent underwriting, delayed approvals, static pricing models, reactive collections, and limited portfolio visibility start affecting both growth and risk performance.

CapitalBridge’s experience showed that improving decision systems can help NBFCs reduce defaults, improve pricing quality, speed up underwriting, strengthen portfolio monitoring, and maintain growth momentum.

Sound Familiar? Run the Same Diagnostic

If your lending teams are dealing with rising defaults, inconsistent underwriting decisions, slow approval cycles, limited early-warning visibility, and pricing inefficiencies, these are often signs that operational risk is building inside the lending workflow itself. The same diagnostic approach used with CapitalBridge can help you identify where those gaps exist. You will have a clear view of how they are impacting decision-making, pricing, and portfolio performance.

Schedule a 30-minute Risk Intelligence Audit with the Mindwebs team to review:

Current underwriting workflows
Portfolio monitoring gaps
Pricing decision structures
Operational bottlenecks in risk assessment

[Schedule your 30-minute DataSense Audit → https://calendar.app.google/aXktZEMrNnV3n8yC7 ]

Reach out at [email protected] or visit mindwebs.org for a closer look at the platform.