By Alexander Yanchuk, owner of a consulting company, management consultant in the field of organizational development, lean manufacturing and quality management;
By Natallia Karnialiuk, junior researcher
Approach
2025 is a tipping point. Artificial intelligence in lending is no longer “something someone somewhere implemented.” It has become the new normal, the new architecture of decision-making. AI is no longer “integrated”; it is being built directly into processes. Not as an external module, but as the foundation for everything — from pre-approved offers to collection strategies.
Banks that understood this are already saving millions on scoring, increasing LTV through personalization, and reducing churn. The rest are drowning in regulations, Excel spreadsheets, and deadly bureaucracy.
So, financial institutions fall into two categories:
- Those who’ve already switched to real-time AI decisioning and manage risk in minutes, not weeks.
- Those who still gather their credit committee on Fridays at 5 PM to discuss what happened on Tuesday.
AI isn’t an “innovation.” It’s a way to do the same thing faster, more accurately, and with less risk. If you didn’t make it — you’ll end up supplying your client base to those who did.
Problem
The biggest lie on the market: “Everything’s working fine.”
Here’s the reality:
1.Scoring models are outdated. 80% of players still use logistic regression and basic rule-based systems. No personalization, no real behavioral insight beyond three banking products.
2.Data is poor. Banks swim in data but don’t know how to use it. 70% of the features used to train models are irrelevant. No feature store, no framework for feature selection, everything is manual.
3.Explainability is missing. Models are a black box. No one can explain why Ivanov was denied while Petrov was approved. Which means: no trust, no regulatory protection, no resilience.
4.AI initiatives don’t scale. Pilots take 6 months. In production — one model. The rest exists only in PowerPoint for the CEO.
Add to that:
- ML departments aren’t embedded in the business.
- Compliance blocks every step.
- Legacy IT kills any speed.
- And most importantly — no one is responsible for the outcome.
| Problem | Description | Consequences |
|---|---|---|
| Outdated scoring models | Logistic regression, no behavioral analysis | Wrong approvals/declines |
| Poor data quality | Irrelevant features, no feature store | Reduced accuracy |
| Lack of explainability | Black-box models, no decision transparency | Regulatory risk, loss of trust |
| Not embedded in business | AI team works in isolation | No impact on P&L |
Example?
One major Eastern European bank spent $3M implementing an AI scoring system. After 18 months, the model was live on a single product. The result? +2.5% in accuracy, 0% scalability, complete distrust from risk management.
Solution
AI in lending is not about models. It’s about architecture.
Here’s what a working AI ecosystem looks like in 2025:
1.Explainable AI (XAI) — transparency or death. Use models that can be interpreted at the decision level. This is not a trend — it’s a regulatory requirement in the EU, UK, Singapore, and soon everywhere. SHAP, LIME, TrustyAI — must-haves.
2. Feature store + automated data processing pipeline. Features aren’t built manually in Jupyter; they are managed by a system. Selection is based on business impact, not correlation with default.
3. Hybrid models. A blend of decision trees (XGBoost), neural networks, and business rules. Each model in its place: neural nets for behavior, trees for explainability, rules for regulation.
4. Real implementation, not a showcase. The model lives inside the product, makes real-time decisions, has feedback loops, retrains on schedule, and is versioned.
5. End-to-end integration with processes. Scoring isn’t a standalone block. It coexists with antifraud, limit offering, terms, disbursement channel, and servicing method.
6. AI in collections. Predictive models tell you who to call, how to talk, when to offer restructuring, and which words to use in the script. This reduces delinquency and boosts agency efficiency.
7. AI in upsell. The model recommends not just the next product, but the product the client is 74% likely to accept when offered via push notification rather than a call. This isn’t CRM. This is a smart bank.
| Component | Legacy AI | AI 2025-Ready |
|---|---|---|
| Feature storage | None, built manually in Jupyter | Managed Feature Store with versioning |
| Model updates | Manual, once per quarter | Automated retraining every 72 hours |
| Decision transparency | Black-box | SHAP / LIME explainability built-in |
| Deployment | Pilot/demo only | End-to-end integration in live products |
| Performance measurement | Missing, no business impact tracked | P&L-driven KPIs, speed-to-decision metrics |
Case: HDFC Bank, India
In 2024–2025, HDFC Bank built its own AI platform based on AWS SageMaker and Databricks.
What they did:
– Retrained models every 72 hours
– Abandoned manual scoring
– Built their own feature store from 1.2 billion transactions
– Embedded explainability in every rejection
Results:
– Decision speed: 2.7 seconds
– Approval rate up by 9% with no increase in default
– Risk cost down by 18 bps
– NPS in lending up by 14 points
Conclusions
AI is not a module. It’s the core architecture of the lending business.
If your system includes AI but it doesn’t impact P&L — you haven’t implemented AI.
If your models are opaque, unjustified, and don’t live inside your products — it’s a toy.
The future belongs to those who build "banks as platforms", where decisions are not made by people in Zoom, but by models in production.
Every day without AI in prod is lost revenue, increased risk, and lost customers.
Sources
– McKinsey & Co. "The State of AI in Risk 2025"
– BCG AI in Lending Index (Q2 2025)
– World Bank Fintech Regulation Tracker
– Internal benchmarks: HDFC, DBS Bank, Capital One
– Interviews with data scientists from top-10 fintechs
– Our own AI deployment cases in real banks