AI in banking & insurance: from use case to production
Fraud detection, compliance, enhanced customer relationships: deploy AI within a well-controlled regulatory framework, with measurable results from the very first months.
Artificial intelligence is redefining the banking and insurance professions: real-time fraud detection, automation of compliance processes, customer relationships enhanced by generative AI. Institutions that deploy AI in a structured way — with rigorous governance and full traceability — achieve measurable operational gains while keeping the regulatory risks specific to the financial sector under control.
The financial sector concentrates considerable volumes of data — transactions, contractual documents, customer interactions, market data — which constitute an ideal terrain for AI. Yet most institutions struggle to move beyond the POC stage. The reasons are well known: data silos, technical debt in legacy systems, strict regulatory requirements and difficulty recruiting the necessary skills. Our approach consists of identifying high-impact use cases and bringing them into production within an industrialized framework, relying on existing teams.
The arrival of generative AI opens up new prospects for the sector: advisor support, document analysis, regulatory synthesis, report generation. But it also introduces new risks (hallucinations, data leaks, bias). This is why we support our clients with an approach that incorporates governance, explainability and model monitoring from the outset. Every deployment is governed by performance and risk indicators monitored continuously.
Based in Villeneuve-d'Ascq, at the heart of the Lille technology ecosystem, we work with banks, insurers and mutual insurance companies of all sizes. Our team combines sharp technical expertise (MLOps, LLMOps, data engineering) and a deep understanding of the business and regulatory constraints of the financial sector. We work both on strategic framing missions and on operational development sprints.
Industry-specific challenges
The constraints we factor into every engagement.
Model traceability and explainability
Regulators (ACPR, ECB) require full traceability of automated decisions. Every model must be able to justify its predictions in a way that is intelligible to auditors, customers and business teams. A lack of explainability exposes the institution to sanctions and erodes stakeholder trust.
Data sensitivity and governance
Banking and insurance data are among the most sensitive: personal data, health data, transaction histories. Their use by AI models requires a strict framework of governance, pseudonymization and access control compliant with GDPR and sector-specific requirements.
Model risk and robustness
A scoring or fraud detection model that drifts can generate considerable financial losses or unjustified service refusals. Model risk management requires continuous monitoring, stress testing and proven rollback procedures.
Evolving regulatory compliance
Between the European AI Act, the EBA's machine learning guidelines, and the ACPR's recommendations, the regulatory framework is evolving rapidly. Teams must anticipate these changes to avoid deploying solutions that will become non-compliant in the short term.
Integration with legacy systems
Banking information systems often rely on older architectures (mainframes, heterogeneous databases). Integrating AI models in real time into these environments requires specific engineering and a pragmatic approach to avoid tunnel-effect projects.
High-impact use cases
The AI applications that generate value in your industry.
Real-time fraud detection
Deployment of machine learning models on transaction streams to identify suspicious operations in real time. The algorithms analyze hundreds of signals (amount, location, frequency, historical behavior) and generate prioritized alerts for fraud analysts.
40 to 60% reduction in false positives and detection of complex fraud schemes invisible to traditional business rules.
KYC automation and customer onboarding
Use of computer vision and NLP to automatically extract, verify and cross-check identity documents, supporting evidence and compliance data when establishing a relationship. The process reduces onboarding times while strengthening the reliability of controls.
KYC processing time reduced by 70%, rejection rate for non-compliant documents divided by 3.
Customer support enhanced by generative AI
Deployment of a conversational assistant based on an LLM, connected to internal document repositories (general terms and conditions, product sheets, regulatory FAQs) via a RAG architecture. The agent answers questions from customers and advisors with verifiable sources.
First-contact resolution rate improved by 35%, average handling time reduced by 45%.
Automated compliance and regulatory monitoring
Automatic extraction of regulatory obligations from official texts (Official Journal, European directives, ACPR circulars) and mapping to internal processes. Compliance teams receive targeted alerts and preliminary impact analyses.
Monitoring coverage extended to 95% of relevant sources, processing time reduced from several weeks to a few days.
Intelligent claims management
Automation of the triage, qualification and estimation of claims through document analysis (reports, photos, expert appraisals) using vision and NLP models. Simple cases are processed automatically, complex cases are routed to experts with a preliminary assessment.
Average processing time for simple claims reduced by 60%, customer satisfaction up by 20 NPS points.
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