Cashe Financial Credit Risk Analyst Interview: Rounds, Questions and Preparation

What the job involves

A Credit Risk Analyst in neobanking, lending, and insurtech evaluates borrower creditworthiness using alternative data, digital footprints, and traditional bureau scores to approve or reject loan applications. Day to day involves building and monitoring risk models, setting underwriting policies, tracking portfolio delinquency, and working with product and data teams to balance growth with credit losses.

Role and Technical Knowledge

1. 1

   What is the difference between credit risk and market risk in a lending business?

   Credit risk is the chance a borrower defaults on a loan, while market risk is loss from market price movements like interest rates or equities. Focus on how neobanks primarily manage credit risk through underwriting and collections.

2. 2

   Explain the 5 Cs of credit and how you would apply them to a small business loan applicant in a digital lending platform.

   Cover Character, Capacity, Capital, Collateral, and Conditions. For digital lending, mention alternative data like GST filings, bank statement analysis, and digital transactions to assess each C quickly.

3. 3

   What are PD, LGD, and EAD, and how do they combine to estimate expected credit loss?

   Define Probability of Default, Loss Given Default, and Exposure at Default. Expected Credit Loss = PD x LGD x EAD. Give a simple numeric example to show you understand the formula.

4. 4

   How do RBI digital lending guidelines affect how a neobank like Cashe might onboard and underwrite customers?

   Mention requirements like direct disbursal to borrower bank accounts, transparent fees, consent for data usage, and reporting to credit bureaus. Show awareness that compliance shapes product design and risk controls.

Practical Scenarios and Data Analysis

1. 5

   You notice that the 30+ DPD rate for a new personal loan product has doubled in the last two months. How would you investigate?

   Break down by vintage, channel, credit score band, geography, and loan size. Check for policy changes, data quality issues, or external shocks. Suggest corrective actions like tightening filters or adjusting limits.

2. 6

   Given a sample dataset of loan applications with features like income, age, and past delinquencies, how would you build a simple scorecard?

   Outline steps: data cleaning, exploratory analysis, binning, logistic regression, validation, and setting cut-offs. Mention using WOE and IV for feature selection and monitoring for stability.

3. 7

   A customer with a good bureau score defaults on a small ticket loan. What could be the reasons and how would you prevent similar cases?

   Consider thin file, recent credit-seeking behaviour, income instability, or fraud. Suggest using alternative data like utility payments, device data, or bank statement analysis to catch invisible risks.

4. 8

   Write a SQL query to find the percentage of loans that are 90+ days past due for each product type in the last quarter.

   Use GROUP BY on product type, filter by date and DPD, and calculate ratio. Be ready to explain joins with a loan table and a repayment table.

Behavioural and Situational Questions

1. 9

   Tell me about a time you had to make a decision with incomplete information. How did you handle it?

   Use STAR format. Highlight how you identified key data points, assessed risk, and took a calculated decision. Relate it to credit risk where perfect data is rare.

2. 10

   You are under pressure to approve more loans to meet growth targets, but you see signs of rising risk. What do you do?

   Show you can balance business goals with risk discipline. Suggest presenting data to stakeholders, proposing segmented approvals, and monitoring closely rather than blanket approval.

3. 11

   Describe a time you worked in a team to solve a problem. What was your role?

   Focus on collaboration, communication, and your specific contribution. For a fresher, a college project or internship example works if it shows analytical thinking.

4. 12

   Why do you want to work in credit risk at a fast-growing neobanking company like Cashe?

   Connect personal interest in data-driven decisions and financial inclusion. Mention eagerness to learn underwriting, alternative data, and regulatory aspects in a dynamic environment.

HR and Company Fit

1. 13

   What do you know about Cashe's business model and how it differs from traditional banks?

   Research Cashe's short-term personal loans, app-based process, and use of technology for quick disbursals. Highlight speed, convenience, and targeting underserved segments.

2. 14

   How do you stay updated on credit risk trends and regulations in India?

   Mention following RBI circulars, credit bureau reports, fintech news, and LinkedIn groups. Show curiosity by citing a recent change like account aggregator framework.

3. 15

   Where do you see yourself in three years in this role?

   Show ambition to grow into a senior analyst or risk manager, deepening expertise in credit modeling and portfolio strategy. Align with company growth.

4. 16

   What are your salary expectations for this fresher role?

   Research typical entry-level analyst salaries in Indian fintech (e.g., 3-6 LPA) and give a range. Emphasise learning and growth over exact number.