Credit scoring with insufficient variables
Approval models based on traditional variables that undervalue creditworthy customers with no formal credit history, or miss early deterioration signals in the existing portfolio.
Banks, savings banks, and fintechs process millions of transactions, credit decisions, and customer interactions with systems that have the data but not the intelligence on top. More precise scoring, fraud detected faster, product offers that arrive when the customer needs them — all on the existing core banking system.
Every financial institution has transactional behavior data, credit history, and usage patterns that could predict risk, detect fraud, and personalize offers. The problem is that data sits in silos — and the models that would exploit it have been on the roadmap for years without launching.
Approval models based on traditional variables that undervalue creditworthy customers with no formal credit history, or miss early deterioration signals in the existing portfolio.
Fraud patterns continuously evolving — card, wire, account takeover — while rules-based systems update weeks behind.
Mass campaigns with low conversion because the product reaches the wrong segment at the wrong time. The customer sees the mortgage offer three months after they needed it.
Inquiries about transactions, products, and operations saturating the call center and app with questions a model could resolve in seconds.
Five applications with documentable ROI at 12-18 months. All use cases require transactional data and customer history — which in banking, except for very young fintechs, already exists.
Models enriching traditional scoring with transactional behavioral variables, account usage patterns, payment behavior signals, and alternative data sources. Better discrimination between good and bad payers — and access to under-banked segments with classical scoring.
Early delinquency reduced 15-25% in new portfolio where the model is applied. Approval rate improved for creditworthy customers without formal history.
Models scoring each transaction in milliseconds against the account holder's normal behavior patterns: amount, time, geolocation, velocity, and sequence. Suspicious transactions are blocked or challenged — normal ones generate no friction.
Fraud detection rate improves 40-60% versus static rules systems. False positives (blocked legitimate transactions) reduced — less friction for the customer.
A product propensity model (mortgage, personal loan, funds, insurance) trained on customer behavior: salary changes, savings movements, upcoming maturities. The relationship manager receives the right offer for the right customer at the right moment.
Campaign conversion improves 25-40% versus non-personalized campaigns. Customers contacted with relevant offers — not the full catalog.
An assistant on app, web, and WhatsApp resolving inquiries about transactions, balances, products, operations, and complaints. It knows the customer context and transfers to a human manager when negotiation or real complexity arises.
55-70% reduction in simple call center inquiries. Banking app with instant resolution of the 80 most frequent questions. Managers spend time on high-value clients.
Models detecting credit deterioration signals in the existing portfolio: spending pattern changes, increased credit utilization, payment irregularities. The manager receives alerts 60-90 days before a missed payment appears.
Recovery rate in early management is 3-5x higher than post-default recovery. Provisions reduced through lower delinquency in proactively managed portfolios.
We work with the institution's systems (Temenos, Finastra, nCino, Salesforce Financial Services, proprietary systems) via API or data lake. All scoring and fraud models are designed with explainability (required by European banking regulation) and documented variable usage — auditable by the compliance officer and regulator.
Audit of available data, credit and transactional history quality, and critical processes. We identify the 2-3 cases with ROI defensible to the risk committee.
Implementation of improved scoring, fraud detection, or portfolio monitoring on a defined product or segment.
For institutions wanting AI consolidated in risk, fraud, offers, and service on a common data architecture.
A 30-minute call about your product portfolio, data quality, and specific pain points in risk or fraud. We leave with 2-3 candidate cases and a 12-18 month ROI estimate.