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Deep banking client log analysis, contextual workflow triggering: product reco, fraud alert, credit opportunity, complaint management.
AI orchestration layer for banking players: deep client log analysis (transactions, interactions, life events, risk signals) and dynamic contextualized workflow triggering. Product recommendation (savings, credit, insurance) at the right time, fraud alert based on behavior deviation, contextual complaint management, upsell or cross-sell opportunity at the opportune moment. Designed for strict regulatory compliance (KYC, GDPR, banking secrecy, AML).
Reference anonymized under confidentiality. Details available on request under NDA.
A bank customer who receives the right proposal at the right time (savings when they have surplus cash, credit when they need it) feels understood, not solicited. A customer whose complaint is handled by an advisor who immediately sees their context feels considered. The doctrine applies to the sector: a customer who feels served stays, a customer who feels tracked leaves. Expected impact: improved Net Promoter Score, decreased churn rate, increased product equipment per customer.
Client log analysis (income, expenses, events, life stages) and automatic triggering of relevant proposals: savings at the right time, mortgage detected by signals, insurance linked to a project, investment adapted to profile.
The analysis engine continuously compares behavior to client baseline and triggers an alert workflow (client notification, temporary block, advisor contact) on significant deviations. Reduces fraud and false positives.
The advisor receiving a complaint immediately sees the complete history, concerned transactions, previous interactions. The workflow proposes the adapted resolution scenario. Processing time divided by two to three.
Automatic suggestions based on profile and history: premium card for a high-volume client, home insurance after a mortgage, wealth management for a detected inheritance. Tact and timing mastered.
This product applies to the following business functions. Click to discover how we adapt it to each.
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A banking CRM stores and displays information. Our solution continuously analyzes the log and triggers workflows. It is an orchestration layer above the CRM, not another CRM. Contextual decision engine approach, not enriched database.
The architecture natively integrates constraints: KYC for client knowledge, GDPR for personal data protection, banking secrecy (compartmentalization, logging, access), AML (anti-money-laundering, signal alerts), compliance secretariat. Independent audit planned by contract.
Integration-oriented architecture: we connect to main core banking (Temenos T24, Avaloq, Finastra, and proprietary systems) via their APIs. The analysis engine runs in parallel, without modifying the reference system.
Typical indicators on similar deployments (to be confirmed per your scope): cross-sell conversion rate improved by 15 to 30%, fraud detection rate improved by 20 to 40%, complaint processing time divided by two. KPIs defined at initial scoping.
Six to twelve months depending on scope (recommendation only, fraud only, complete scope) and core banking integration complexity. Pilot on a client sub-segment in three to four months, then extension.
Common conceptual architecture (orchestration layer on log analysis and dynamic workflows), but distinct sectoral declensions: data sources, relevant signals, regulatory compliance, and business workflows are sector-specific. No direct reuse from one sector to another.
Free initial scoping, 30 minutes to 2 hours, with a solution lead adapted to your sector.