Banking

AI in banking: less fraud, more conversion on personalized offers.

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.

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Serving financial institutions and fintechs in Spain

The Challenge

In banking, risk and opportunity live in the same data.

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.

  • 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.

  • Fraud that scales faster than detection

    Fraud patterns continuously evolving — card, wire, account takeover — while rules-based systems update weeks behind.

  • Product offers without real personalization

    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.

  • Customer service that cannot scale

    Inquiries about transactions, products, and operations saturating the call center and app with questions a model could resolve in seconds.

Use Cases

Where AI moves the margin of a financial institution

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.

  • 01

    Assisted credit scoring

    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.

    What changes

    Early delinquency reduced 15-25% in new portfolio where the model is applied. Approval rate improved for creditworthy customers without formal history.

  • 02

    Real-time fraud detection

    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.

    What changes

    Fraud detection rate improves 40-60% versus static rules systems. False positives (blocked legitimate transactions) reduced — less friction for the customer.

  • 03

    Personalized financial product offers

    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.

    What changes

    Campaign conversion improves 25-40% versus non-personalized campaigns. Customers contacted with relevant offers — not the full catalog.

  • 04

    Conversational banking assistant

    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.

    What changes

    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.

  • 05

    Portfolio monitoring and early warning alerts

    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.

    What changes

    Recovery rate in early management is 3-5x higher than post-default recovery. Provisions reduced through lower delinquency in proactively managed portfolios.

Our Approach

On top of your core banking and existing data. With compliance from day one.

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.

  • Data and Opportunity Diagnosis

    3-4 weeks

    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.

    • Credit and transactional data quality review
    • Architecture analysis and integration possibilities
    • Prioritized use case catalog
    • Compliance framework (GDPR, EBA guidelines, explainability)

  • Pilot: Single Risk or Fraud Model

    10-16 weeks

    Implementation of improved scoring, fraud detection, or portfolio monitoring on a defined product or segment.

    • Data pipeline from core banking
    • Model trained and validated on proprietary history
    • Variable documentation and logic (explainability)
    • Backtesting and statistical validation
    • Integration with decision workflow or alerts
    • 8-week live production impact measurement

  • Financial Intelligence Platform

    6-10 months

    For institutions wanting AI consolidated in risk, fraud, offers, and service on a common data architecture.

    • Unified data architecture (feature store)
    • Multiple models with centralized governance
    • Integration with regulatory systems (COREP, FINREP)
    • MLOps framework for retraining and drift monitoring
    • Ongoing support and model review

Results

What changes at the institution at 12 months

  • Early delinquency reduced 15-25% in new portfolio managed with improved scoring.
  • Fraud detection improved 40-60% with simultaneous false positive reduction.
  • Product campaign conversion 25-40% higher with behavioral personalization.
  • Call center with 55-70% fewer simple inquiries — managers focused on high-value clients.
  • Early deterioration alerts 60-90 days before missed payment — recovery 3-5x more effective.
  • Documented regulatory compliance: auditable models, integrated explainability.

FAQ

What risk and technology directors ask

  • Do the models comply with European banking regulation (EBA)?

    Compliance is a design constraint. Scoring and fraud models are developed with documented explainability (variables used, relative weight, decision logic), formal statistical backtesting, and documentation for regulator or internal compliance officer audit. EBA guidelines on ML use in credit are part of the development framework.

  • Can we use transactional behavioral data without GDPR issues?

    With appropriate legal basis (legitimate interest for fraud, contract execution for scoring), yes. The institution's DPO participates from the start to validate the legal basis and treatment. Data does not leave the institution's perimeter.

  • What if our data is in silos across systems?

    Data consolidation is almost always the first deliverable in banking projects. The diagnosis maps exactly which data is in which silo and proposes the integration architecture. It is not a blocker — it is project work.

  • What does it cost to start?

    Diagnosis: €8k-€15k. Pilot (scoring or fraud on a product): €45k-€90k depending on volume and complexity. Full platform: €150k-€300k+ over several months. In mid-sized institutions, ROI from avoided fraud or reduced delinquency typically covers the pilot in year one.

  • What if we already have a proprietary scoring model?

    A perfect starting point. We audit it, identify where it is leaving value on the table (under-scored segments, variables it does not use, deterioration signals it misses), and propose incremental improvements or a challenger model running in parallel before replacing the current one.

Want to see which use cases apply to your institution?

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.

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