02 / SELECTED WORK PROJECT
TJSB SAHAKARI BANK
Assessing whether TJSB’s credit risk controls, asset-quality management and capital structure were adequately supporting sustainable financial performance.
The problem
TJSB’s diversified and expanding loan portfolio required an assessment of whether its credit risk controls, asset-quality management and capital structure were adequately supporting sustainable financial performance.
Approach
We conducted a five-year diagnostic of TJSB’s credit management framework, covering architecture, portfolio structure, risk appetite, asset quality, income, capital and statistical validation.
Studied the bank’s risk department, credit approval structure, risk grading, exposure limits, collateral management, monitoring and restructuring mechanisms.
Assessed short-, medium- and long-term lending trends to understand how TJSB’s credit portfolio was changing.
Evaluated borrower concentration, sector exposure and other quantitative risk limits.
Tracked Gross NPA, Net NPA and provisions against NPAs.
Examined interest earned from loans and advances as an indicator of lending performance.
Analyzed Tier 1, Tier 2, RWA and CRAR to evaluate capital resilience. The 2024 position showed Tier 1 capital of ₹1,261.59 Cr, total capital of ₹1,445.51 Cr and CRAR of 17.57%.
Tested whether credit risk — specifically NPAs — had a statistically meaningful relationship with interest income.
The business
TJSB Sahakari Bank Ltd. began operations in 1972 and developed from its first branch in Thane into a cooperative banking network across several Indian states. The work considered its emphasis on customer service, community engagement, financial inclusion and technology adoption.
What we delivered
Risk system
Credit risk policy
The bank's framework includes structured credit appraisal, separation of origination, evaluation and approval, exposure ceilings, collateral evaluation, ongoing monitoring and policy review.
Data from the project
Figures below are taken from the project material and presented for financial context.
The project examined short-term, medium-term and long-term loan classification across FY 2019–20 to 2023–24. Exact segment values are not reproduced here where not provided as discrete chartable series in the source material.
Interest generated from loans (₹ lakh).
Gross non-performing assets (₹ lakh).
CRAR over the reported period.
Capital structure · 2024
KPIs & advisory value
Key indicators used across the diagnostic — what each metric showed, and where it informed advisory judgment.
| KPI / Metric | What we evaluated | Where we created advisory value |
|---|---|---|
| Gross NPA | ₹33,057L → ₹28,793L over the period | Asset-quality & credit-risk health |
| Net NPA | Tracked alongside Gross NPA | Net stressed assets after provisions |
| NPA Provisions | Provisioning against stressed loans | Loss absorption / prudence |
| Interest Earned | ₹58,018.75L → ₹71,841.40L | Lending income performance |
| Loan Mix | Short / medium / long-term loans | Portfolio structure & maturity risk |
| CRAR | 15.23%–17.57% range | Capital adequacy & resilience |
| Tier 1 Ratio | ~12.1% → 15.34% | Quality of capital |
| Tier 2 Ratio | ~2.2%–3.3% | Supplementary capital |
| RWA | ₹6,253.35 Cr → ₹8,226.27 Cr | Risk-weighted balance-sheet growth |
| Borrower Exposure | Individual / group concentration limits | Concentration risk |
| Sector Exposure | Sector-wise lending limits | Sector concentration risk |
| Pearson Correlation | r = −0.589 | NPA vs. interest-income relationship |
| p-value | 0.296 | Statistical significance of relationship |
Project methodology
As part of the project methodology, we examined the relationship between Gross NPAs and Interest Earned.
H₀: There is no significant relationship between credit risk management and financial performance.
H₁: There is a significant relationship.
The observed negative correlation was not statistically significant at the conventional 0.05 level. There was not sufficient evidence to reject the null hypothesis based on the five-year dataset. The project material notes that limited sample size and other factors may influence interest income.
Key insight
The project brought multiple financial and risk indicators together to provide a clearer view of the relationship between credit risk and financial performance.
The work identified a layered approach involving credit approval, exposure controls, monitoring and mitigation.
The analysis showed CRAR increasing to 17.57% in 2023–2024.
The methodology shows that financial relationships cannot always be established from a small dataset alone.