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DexLab Analytics

Name: DexLab Analytics
Address: K-3/5, DLF Phase 2, Behind Central Arcade, Gurugram, 122002, IN
Telephone: +91 93157 25902
Price range: ££

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DexLab Analytics

DexLab Solutions has been founded by a team of core industry professionals as a provider of accelerated learning destination for anyone.

Our consultants have in-depth practical understanding of the subject, are well versed with the various e-learning platforms as well as passionate about imparting skills and knowledge to others in a lucid and practical manner. You will find our consultants to be approachable, easy to follow and very helpful in actual industry practices. All our trainers display the following distinctions:

They have proficient and in-depth knowledge of SAS (Base & Advance) BI Tool; R Statistical Analysis Platform, & Analytics (Core & Predictive Modeling). You can be rest assured that you are learning from seasoned core industry experts. They also posses extensive knowledge in SPSS, VBA and Base Excel. Our trainers have actively contributed to the creation of the curriculum and other training materials. This enables them to be actively involved in the process of end to end training, rather than just teaching. Each one has contributed to the designing of the training modules, schedules etc. giving them an edge over others in the industry. Our trainers are capable of fine tuning their teaching methods and style to meet unique needs of the students, depending on their level. They work hard towards simplifying the theoretical concepts and make them easy to follow and implement. They make technical content simple and easy. They have proven expertise on having delivered knowledge and systems-based training programs. You will find them displaying good attitude, high energy and solid team players all through.

31/10/2026

How do you build an ECL model in Python? 🧮
Stage the loans, estimate PD, LGD and EAD, discount at the EIR, then weight your scenarios.

Our new worked example does it end to end on synthetic data, with code, for IFRS 9 and Ind AS 109.
Read it here: https://www.dexlabanalytics.com/blog/ecl-model-in-python

27/10/2026

Why is one economic forecast not enough when banks calculate expected credit losses?

Because losses don't rise in a straight line with the economy. In our illustrative example, a base case alone gives ₹24,000 on a ₹10 lakh loan. Weighting base, upside and downside scenarios gives ₹32,800.

With RBI's ECL framework for banks starting 1 April 2027, this is becoming a core skill for Indian credit risk teams.

Our new guide explains how macroeconomic scenarios work in ECL models, how they affect staging, and what IFRS 9, Ind AS 109 and RBI expect:
https://www.dexlabanalytics.com/blog/macroeconomic-scenarios-in-ecl-models

23/10/2026

Quick quiz for finance and risk folks 👇

A bank lends ₹10 lakh. PD is 2%. LGD is 40%. What is the expected loss?

A) ₹20,000
B) ₹8,000
C) ₹40,000
D) ₹4,00,000

Drop your answer in the comments. The full working is in our new post: https://www.dexlabanalytics.com/blog/expected-loss-calculation-formula-examples

13/10/2026

What would your loan book lose in a deep recession? 📉

Expected loss is the weather forecast. A stress test is the building code: it checks the roof survives the once-in-fifty-years storm.

Our new guide explains how banks stress test credit risk, from Basel and RBI frameworks to a worked example showing how a 2% default probability can jump to about 8.57% in a bad year.

Perfect for risk analysts, validators and finance students.

👉 Read it here: https://www.dexlabanalytics.com/blog/stress-testing-credit-risk

06/10/2026

A question for finance students and banking professionals 👇

Your CIBIL score and a company's CRISIL rating both measure credit risk. So why do banks need both?

Because they're built for very different borrowers:

✔️ A credit score ranks thousands of individuals using statistical scorecards. Fast, automated, data-driven.
✔️ A credit rating grades a few large companies using financial ratios and expert judgement. Slower, deeper, borrower-specific.

From April 2027, when RBI's ECL framework kicks in, rating downgrades will feed directly into how much banks set aside for expected losses.

We've broken it all down in our latest guide:

Which did you learn about first: credit scores or credit ratings? Tell us in the comments.

03/10/2026

Why do credit risk teams never judge PD, LGD or EAD in isolation?
In 2008, Lehman Brothers' bondholders recovered just 8.625% of what they were owed (BIS Quarterly Review, December 2008). Basel's Foundation IRB approach assumed a 45% loss on claims like these. The actual loss was closer to 91%.
This 30-second reel breaks down the formula at the heart of every credit risk framework:
Expected Loss = PD × LGD × EAD
PD tells you how likely default is. LGD tells you how much is lost after recoveries. EAD tells you how much is owed when default happens. In a downturn, all three tend to get worse together, which is why banks stress them together.
For Indian banks, this is about to become routine. RBI's ECL directions take effect on 1 April 2027, and they run on exactly these three parameters.
Read the full guide: https://www.dexlabanalytics.com/blog/credit-risk-framework-pd-lgd-ead

01/10/2026

Is machine learning replacing the traditional credit scorecard? Not yet.

Gradient boosting can spot patterns that logistic regression misses. But banks must explain their credit decisions to customers and regulators, and complex models are harder to explain and validate.

That's why many banks use machine learning as a "challenger" model alongside the scorecard, not as a replacement.

Learn how all five types of credit risk models work in our latest guide: [blog URL]

Do you think machine learning will become the main credit model in the next five years? 👇

29/09/2026

Your loan application gets declined. You ask why, and the answer is: "The model decided." 🤔

That answer doesn't work anymore.

Machine learning is becoming common in credit decisions. But lenders still need to explain their decisions in plain language, such as high credit utilisation or too many recent bureau enquiries.

Tools like SHAP make this possible by showing which factors drove each decision. And with RBI's draft model risk guidance released in June 2026, explainability is fast becoming a regulatory expectation.

Want to understand how ML credit risk models really work?
📖 Read our full guide: https://www.dexlabanalytics.com/blog/machine-learning-for-credit-risk-modeling

26/09/2026

Here's a fact that surprises a lot of finance learners.

During the 2007–09 financial crisis, about two-thirds of counterparty credit risk losses didn't come from anyone going bust. They came from CVA: the fall in the value of derivatives as counterparties' credit spreads widened. That's the finding of the Basel Committee on Banking Supervision.

In other words, the counterparty was still paying. The losses showed up anyway.

It's one of the reasons Basel III added a separate capital charge just for CVA.
Read it here: https://www.dexlabanalytics.com/blog/counterparty-credit-risk-definition-management

23/09/2026

RBI requires Indian banks to hold a minimum CRAR of 9% — a full percentage point above Basel III's own global floor of 8%.

Add the mandatory 2.5% capital conservation buffer, and the effective floor becomes 11.5%.

That's just the capital side. RBI also regulates credit risk through provisioning rules (a shift to a forward-looking Expected Credit Loss model just got finalised, effective 2027) and active supervision — stress testing, model validation, and automatic restrictions if a bank's ratios slip.

We've broken down the full framework, including a worked example and what's changing under RBI's newest Directions: https://www.dexlabanalytics.com/blog/how-is-credit-risk-regulated-in-banking

Address

K-3/5, DLF Phase 2, Behind Central Arcade
Gurugram
122002

Opening Hours

Monday	9:30am - 7pm
Wednesday	9:30am - 7pm
Thursday	9:30am - 7pm
Friday	9:30am - 7pm
Saturday	9:30am - 7pm
Sunday	9:30am - 7pm

Telephone

+91 93157 25902

Website

http://www.dexlabanalytics.com/

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