Governance, Risk and Compliance Consulting
AI based Decisioning & Credit Risk Automation
Rayterton helps banks and multifinance institutions move from judgment only credit processes to data driven, AI enabled decisioning. The focus is a credit risk environment that links risk scoring engines, early warning signals, fraud analytics, and customer behaviour insights into one decision layer.
Built on AI and Model Risk Management practices
Designed for retail, SME, and corporate portfolios
This consulting service is directly connected to Rayterton’s AI Governance and Model Risk work. The same governance principles and model lifecycle controls are applied so that automated decisions remain explainable, auditable, and aligned with your credit policies and risk appetite.
AI based credit decisioning
Early warning for NPL and credit risk
Fraud detection for banking transactions
Customer behavioural analytics
Primary owners
Chief Risk Officer, Head of Credit Risk
Key partners
Credit Policy, Collections, Business Unit Heads
Supporting teams
Data Science, IT, Internal Audit, Model Validation
Target organizations include banks and multifinance companies that want to scale credit decisions without losing control of risk and compliance.
Service scope
What the AI based Decisioning & Credit Risk Automation service delivers
The program focuses on four core solution areas, reflecting the original service list: a machine learning risk scoring engine, an early warning system for non performing loans and credit risk, fraud detection for banking transactions, and behavioural analytics for customers.
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Risk scoring engine using machine learning
Design and implementation of credit risk scoring models for application, behaviour, and collection scoring. Models use your historical data and alternative data where available, with clear cut off rules, challenger models, and monitoring dashboards.
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Early warning system for NPL and credit risk
Early warning indicators built on transaction patterns, payment behaviour, and macro signals, producing risk alerts before accounts slip into non performing status. Outputs are linked to collections strategies and relationship manager workflows.
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Fraud detection for banking transactions
Analytics and anomaly detection models that flag suspicious transactions across accounts, channels, and devices. Designed to complement existing fraud rules and monitoring systems, reducing false positives and enabling faster investigations.
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Behavioural analytics for customers
Customer behaviour segmentation and lifetime value analytics that support differentiated credit limits, pricing, and treatment strategies. Insights are connected to marketing, cross sell, and risk limit decisions.
Target organizations and teams
Who typically engages Rayterton for AI based Decisioning
This service is aimed at institutions that want to industrialize AI in credit risk, while keeping strong governance and human oversight on final decisions.
Banks (retail, SME, and corporate segments)
Multifinance and consumer finance companies
Digital banks and fintech lenders
Risk, Analytics, and Credit Policy teams
Engagement model
How Rayterton typically runs an AI based decisioning program
The engagement is structured so that AI models are tested in a safe sandbox, validated under Model Risk Management principles, and then rolled out to production with clear guardrails.
Phase one
Assessment and design
Rayterton works with risk, business, and analytics teams to define decision points, data readiness, and model governance requirements.
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Assessment of current credit decision processes, scorecards, and early warning practices across products.
2
Data discovery and feature design, including which sources will be used for risk scoring, early warning, and fraud analytics.
3
Agreement on model use cases, governance checkpoints, and success metrics for pilots and production roll out.
Phase two and three
Model build, integration, and pilot
Models are developed, validated, and integrated into decision flows, with pilots run on real portfolios before scaling up.
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Development and backtesting of machine learning risk scoring, early warning, fraud, and behaviour analytics models.
5
Implementation of decision rules, override thresholds, and monitoring dashboards in existing loan origination and servicing systems.
6
Pilot rollout on selected segments, followed by wider deployment once performance, fairness, and stability criteria are met under Model Risk Management standards.
Value for your organization
What you get from Rayterton
The value model is consistent with other Rayterton consulting programs. You receive a working AI based decisioning setup that already reflects your credit policies and data environment before you commit to a full scale transformation.
Before go live
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Free customization for key decision rules, monitoring reports, and alert configurations needed by your risk teams.
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Sandbox environment with sample portfolios where risk scoring, early warning, fraud, and behavioural analytics can be tested end to end.
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Support to migrate historical performance data for backtesting and calibration of the AI models.
After go live
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Annual maintenance that includes model performance review, recalibration requests, and updates to decision rules without extra man day cost for standard changes.
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Monitoring and performance tuning for application and database so that scoring and alerts can run within required response times.
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Optional integration with other Rayterton platforms such as Loan Origination, ERM Digitization, AI Governance, or Digital Risk Management.
Ready to scale AI based credit decisioning for your institution
Share your current credit policies, sample portfolios, and key challenges in NPL and fraud. The Rayterton team will prepare a prototype decisioning environment that your credit and risk leaders can test and refine together.
