What RBI’s Draft Guidance Actually Expects Your Model Risk Framework to Cover

TL;DR Most write-ups on model risk management frameworks describe governance, validation, and monitoring in general terms, the way any consulting deck might. RBI’s draft guidance on regulatory principles for model risk management, released June 24, 2026, is more specific than that. It sets out an actual chapter structure: governance, model risk management as a distinct […]

Why Automated Model Risk Management Still Needs Someone to Sign Off

TL;DR A model risk team hears “automated model risk management” and pictures something close to unattended: drift gets caught, evidence gets filed, and nobody has to sit through a status meeting to find out a model is degrading. Half of that picture is accurate. The other half is where teams get into trouble. Automated model […]

Most Model Risk Management Software Doesn’t Do What the Name Promises

TL;DR Search “model risk management software” and most of what comes back is a dashboard with a drift chart and the word “AI-powered” somewhere in the hero copy, a monitoring widget wearing a bigger label than it’s earned. Model risk management software has a specific job: monitor every model in production, including the ones the […]

Risk Modeling in Lending: Methods, Controls, and Where Models Fail

Conceptual illustration of a financial risk scenario showing a melting ice bridge with a falling coin, representing potential lending losses and credit risk.

TL;DR A model can post a strong AUC, pass its backtest, and clear every validation check a team runs on it, and still get the next year wrong. The math was never broken. What broke was the world the model assumed would hold. A Risk Model Can Pass Every Backtest and Still Be Wrong Risk […]

Model Validation: What Independent Review Has to Cover

Four-step human-in-the-loop model control process: anomaly detection, independent authority review, model pause or deactivation, and immutable audit logging.

TL;DR A risk team builds a new collections scorecard, tests it against last quarter’s data, and ships it. The development team calls that testing “validated.” It is not. A model checking its own homework is not validation, it is confidence, and confidence is not what a regulator asks to see. Independent review is what actually […]

Model Risk Management for Indian Banks and NBFCs

Transparent glass piggy bank with marble-like textures on a reflective blue surface, representing financial risk and model risk management.

TL;DR A credit scoring model approves loans every hour of every working day. It was validated once, at launch, eighteen months ago. Since then the borrower mix has shifted, a new product line has fed it data it never saw in training, and its accuracy has slipped by a few points a quarter. Nobody flagged […]

Stress Testing AI Models: What RBI Expects Before You Deploy

3D illustration of transparent glass steps and a glowing circular symbol, representing staged progress and controlled model deployment.

TL;DR Stress Testing Your Balance Sheet and Stress Testing an AI Model Are Two Different Exercises When a risk officer at an Indian bank hears “stress testing,” the reflex is capital adequacy: RBI’s macroprudential scenarios, liquidity coverage under shock conditions, provisioning against a stressed balance sheet. That discipline is well understood, well documented, and audited […]

Model Risk Management for NBFCs: A Configuration Guide for Upper-Layer Compliance

Abstract 3D illustration of stacked regulatory documents enclosed between transparent glass domes, representing regulatory requirements, documentation, and model governance.

TL;DR NBFC-UL Compliance Teams Assume the MRM Draft Gets Stricter at Their Layer Most NBFC-UL risk and compliance teams reading RBI’s June 2026 model risk management draft guidance make a reasonable assumption: because their entity carries the highest regulatory burden under Scale-Based Regulation, the new MRM requirements must scale up with it. Somewhere in the […]

What Your Model Risk Committee Should Actually Be Reporting to the Board

Conceptual illustration of a hammer approaching a transparent protective case containing a telephone, representing risks, safeguards, and governance challenges in model risk management.

TL;DR Most Model Risk Committees Are Reporting Against a Standard That Doesn’t Exist Yet Ask a Model Risk Management Committee chair at an Indian bank or NBFC what their board report should contain, and you will get a confident answer built on habit rather than rule. That reflects where the current regulatory landscape actually stands. […]

Tarika Bhutani

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Tarika is a market development leader driving global growth through strategic partnerships and go-to-market initiatives.

 

She focuses on expanding enterprise adoption of AI solutions across international markets, working closely with partners and clients to enable data-driven transformation.

 

Her work centres on scaling enterprise AI through partner-led growth and direct customer engagement, supporting organisations in implementing impactful, data-driven solutions worldwide.

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Prior to iTuring.ai, he held executive leadership roles at Instant Life, AIG, Nedbank, FNB, and TransUnion. He focuses on enabling enterprises to adopt AI and machine learning within trusted, governed, and risk-managed frameworks.

 

Bryan holds a Master’s degree in Commerce from the University of Johannesburg.

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At iTuring.ai, he oversees product roadmap and customer delivery, applying cloud-first thinking, deep systems expertise, and a focus on building robust, scalable AI solutions that challenge industry norms.

 

He is a graduate of Rajiv Gandhi Institute of Technology.

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Prior to iTuring.ai, he held senior technical roles at Radisys and Aricent. Amit leads platform architecture with a focus on governance, lineage, and traceability.

 

He holds a First Class with Distinction BTech in Computer Science from Cochin University.

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Valsan holds an MBA from the University of Nebraska and a BE in Computer Science from Bangalore University.

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His work includes fraud detection systems saving clients over $19M, patented Customer Relationship Score methodology, and price optimisation recognised by the INFORMS Edelman Award (2014). He has authored multiple research papers and pioneered the data-to-value approach.

 

Suman holds a Master’s in Statistics from CCS HAU and a Bachelor’s in Agricultural Engineering from BHU.