Why Banks Use Internal ML Models

✅ Why Banks Build Internal ML Models (Instead of Relying Only on Fintech Models)

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⭐ 1. Regulatory & Compliance (Most Important)

Banks—especially in India—operate under strict RBI regulations:

🔹 RBI requires:

• Model Risk Management (MRM)

• Validation and periodic re-calibration of ML models

• Auditability & Explainability

• Data residency (must stay inside bank’s environment)

• No black-box decisioning that bank cannot justify

➡️ Fintech-provided models are black-box → bank cannot justify credit decision → high regulatory risk.

So banks MUST have:

• Internal model ownership

• Internal model governance

• Internal monitoring and controls

⭐ 2. Explainability for Credit Underwriting (XAI)

Banks must explain:

• Why loan is approved

• Why loan is rejected

• What feature contributed to risk score

Fintech models often do not expose:

• Model weights

• Feature importance

• Bias metrics

• Documentation for audit

➡️ Bank needs transparent, interpretable models for:

• Underwriting

• Fraud

• AML

• Sanction screening

• Collections

⭐ 3. Data Sensitivity & Privacy

Banks handle:

• PAN, Aadhaar

• Bank statements

• Salary data

• Transaction data

Fintech models = require sharing data outside → not allowed.

Banks require:

• PII masking

• Data minimization

• Internal secure MLOps (Azure ML / AWS Sagemaker)

➡️ They cannot give raw customer data to external fintech.

⭐ 4. Model Customization & Control

Fintech models = generic.

Banks = need bank-specific risk rules, example:

• HDFC → strong salaried customer base

• ICICI → strong credit card penetration

• Kotak → high CASA segment

• SBI → semi-urban and rural profiles

• YES Bank → corporate tilt

Risk ML models must align with:

• Portfolio strategy

• Risk appetite

• NPA tolerance

• Reserve provisioning

Fintech cannot customize to this level.

⭐ 5. Cost Advantage Over Time

Fintech model usage = per API costInternal model = cost reduces as volume grows

Example:

• Sanction screening = ₹1 per API call for external vendor

• Bank does 10M calls/month → ₹1 Cr monthly

Internal model cost after 1 year → far cheaper.

⭐ 6. IP Ownership & Competitive Advantage

Banks do not want to outsource:

• Credit scoring

• Fraud detection

• Early warning

• Collections prediction

These are core differentiators.

Example:

• HDFC’s internal risk engine = major competitive moat

• ICICI’s early warning ML = reduces NPA

• SBI YONO → uses internal behavioral scoring

Fintech cannot provide this advantage.

⭐ 7. Integration Complexity & Latency

Fintech models = over internet = 200–300 msInternal models in AKS/Sagemaker = 10–20 ms

For real-time underwriting:

• uptime ≥ 99.99%

• latency < 50 ms

External fintech cannot meet these constraints.

⭐ 8. Vendor Lock-in Risk

If bank uses fintech as core:

• Pricing changes → big impact

• Vendor bankruptcy → risk

• Model downtimes → SLA breach

Internal models → full control.

⭐ 9. Security & Zero Trust Requirements

Fintech models:

• Do not support bank’s Zero Trust

• Do not meet bank SOC2 + RBI audit

• Cannot integrate with SIEM, logging, anomaly detection

Internal models → end-to-end visibility.

⭐ 10. Model Drift & Continuous Monitoring

Bank must monitor:

• Data drift

• Concept drift

• Bias

• Performance degradation

Fintech does not provide this granular monitoring.

Internal MLOps ensures:

• retraining

• revalidation

• explainability

• fairness

✅ So when DO banks use Fintech ML models?

Banks use fintech ML models for non-regulatory, low-risk, enrichment-type use cases:

Allowed:

• Bank statement analyzer

• GST analytics

• Web/alternative data fetchers

• Video KYC liveliness detection

• OCR/ID document checks

• Email classification

• Lead scoring

• Chatbots

• Collections reminder personalization

Not Allowed:

• Credit decision

• Sanction/AML

• NPA modeling

• Fraud detection core

• Bureau data scoring

• High-value customer profiling

✅

“Banks can use fintech partners for low-risk enrichment use cases, but all regulated, core, and high-risk ML models must remain within the bank due to RBI guidelines, model explainability requirements, auditability, security, and compliance.

As EA/Head of Architecture, my role is to define a hybrid model — external services for enrichment, but internal governed MLOps pipelines for underwriting, AML, fraud, NPA prediction, and risk models. This ensures regulatory compliance, full control, lower long-term cost, and portfolio-specific optimization.”

👉

“For regulated decisions like credit approval, AML and sanctions, the bank must own the primary ML model. Fintech models are used only as enrichment signals, not as the source of truth.”

Delivery KPIs (Architecture Execution KPIs)

Area	Before	After Modernization
Release Frequency	Quarterly	Weekly
Lead Time for Change	3–4 weeks	<48 hours
Automation Coverage	<20%	>90%
Deployment Failure Rate	20–30%	<5%
Environment Provisioning	1–2 weeks	<30 minutes
API Build Time	2–3 months	2–3 weeks
Cloud Infra Scaling	Manual	Auto-scale

Governance KPIs

Governance Area	KPI	Target
Architecture Review Compliance	% designs reviewed	>95%
Security-by-Design Coverage	% apps with threat modeling	>90%
Regulatory Audit Findings	Count per year	Zero critical
Technology Standards Compliance	% aligned	>90%
Shadow IT Reduction	Unknown apps	<5%
Vendor Lock-in Risk	Multi-cloud readiness	100%
Change Success Rate	Successful changes	>95%

👉

“Governance KPIs ensure architecture is not just innovative but safe, compliant, and auditable.”

6. What is GenAI — Leadership Definition (NOT textbook)

❌ Weak Answer:

“GenAI is a model that generates text.”

“GenAI in banking is a cognitive automation layer that sits on top of our digital platforms to convert complex operational, compliance, and customer interactions into intelligent, real-time, contextual decision support and automation.”

Where GenAI Is Used in Banking

• Borrower Assistant (loan guidance, status, agreement explanation)

• Underwriting Copilot (risk explanation)

• AML Investigator Copilot

• Operations Copilot

• Customer Support Automation

• Policy & Compliance Assistant

Difference Between ML and GenAI

ML	GenAI
Predicts	Explains + Generates
Score-based	Reasoning-based
Structured data	Unstructured + structured
Used for decisions	Used for interaction & cognition

👉

“ML decides, GenAI explains and interacts.”

“In our banking modernization, I mapped every business capability like onboarding, lending, fraud, AML, and payments to three layers of KPIs — strategic, delivery, and governance. Strategically, we reduced onboarding from days to minutes, increased digital adoption beyond 80%, and brought down infra cost by 30–40%. On delivery, we moved from quarterly to weekly releases with over 90% automation. On governance, we achieved 95%+ architecture compliance with zero critical audit findings. Architecturally, all APIs are channel-agnostic and serve mobile, web, and branch systems through a common API gateway. For risk decisions, we always retain internal ML ownership for regulatory explainability, while selectively enriching with fintech scores. GenAI sits on top as a cognitive layer — not to replace ML decisions, but to explain, assist, and automate customer and operations journeys with full compliance and auditability.”