System
Core banking
Knows
Transactions, balances, transfers, scheduled operations
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Break the silos between core banking, CRM, claims management, compliance and customer relationship. Deliver the right action at the right time, without tracking the customer. An AI orchestration layer designed for retail banking, private banking, bancassurance, life and non-life insurance, mutuals and brokers, with native KYC, AML, GDPR, banking secrecy and high-risk AI Act compliance.
Banks and insurers have deployed decades of IT investment. Core banking, CRM, KYC, AML, fraud detection, wealth management, credit scoring, policy management, claims management, marketing automation — each system is solid on its own. Regulatory pipelines work, operational processes hold.
But these systems operate in silos. The result is felt on the customer side: ten disconnected solicitations in a week, a credit case handled without the CRM knowing that a request is in progress, a claim that passes through three interlocutors without shared context, a fraud alert that worries instead of protecting, an advisor who does not see the history of the last three contacts. Distrust sets in. Confidence erodes. Attrition rises.
The challenge is no longer to build a new system. It is to make the existing systems talk to each other intelligently, in real time, with a layer that understands context and triggers the right action — without tracking the customer, by serving them better. This logic applies to banking as it does to insurance: the silos are the same, the doctrine is the same.
Transactions, balances, transfers, scheduled operations
Customer interactions (meetings, calls, emails), upcoming appointments
Regulatory identity, supporting documents, AML risk profiles
Suspicious patterns, alerts, temporary blocks
Credit risk, debt capacity, repayment history
Assets, portfolios, performance, investor goals
Segmentations, ongoing campaigns, sent solicitations
Tickets, ongoing complaints, escalations, individual NPS
None of these systems sees the whole picture. The customer pays the price: a mortgage is offered two months after they abandoned an online simulation; they are blocked on a suspected fraud but receive no real-time explanation; they call for a complaint and must retell their story at every transfer; they receive a savings campaign right after an exceptional overdraft signaling a temporary difficulty. Each silo is right, the whole is wrong.
For each type of AI decision in banking or insurance, the level of human involvement differs. The matrix below cross-references main use cases with regulator requirements. Our HITL framework automatically applies the right validation level per case, with complete logging for audit.
| Decision / Case | ACPR / EBA (prudential) | AMF (markets) | GDPR / CNIL | AI Act (EU) |
|---|---|---|---|---|
| Consumer credit decision | Human validation above internal threshold | Out of direct scope | Right to explanation, profiling opposition | High risk: mandatory HITL, compliance documentation, right of appeal |
| Card fraud detection | Incident reporting, documented anti-fraud system | If market-linked | Customer info if automated decision | Limited risk: transparency, monitoring, possible opt-out |
| Financial product advice | Advisory duty, MiFID II, MIFIDPRU profiling | Investor risk profile, suitability test | Explicit consent, right to refuse profiling | High risk: HITL for risky product recommendation |
| AML / money laundering score | Tracfin, mandatory suspicious activity report if threshold crossed | Out of scope | Legitimate interest, documented purpose | High risk: mandatory HITL for account closure |
| Customer complaint handling | Mediation, complaint traceability | If investment dispute | Right of access, rectification, traceability | Limited risk: transparency on AI use |
A software layer that integrates on top of your existing systems. It continuously analyzes the customer log (transactions, interactions, life events, risk signals), scores the context and triggers dynamic workflows. Here are four concrete use cases we deliver for banks.
A customer starts a mortgage simulation on the app, without validating it. Three days later, they check their current account and receive their rent transfer. The orchestration engine cross-references the two signals: recent credit intent, validated debt capacity, stable profile. It triggers a contextual workflow: a message in the customer area (not a call, not a mass email) inviting them to resume the simulation, with a pre-assigned advisor available on the day of their choice. The customer takes back control without being harassed.
Customer log analysis, contextual scoring, core banking and CRM integration, personalized workflow triggering.
The customer feels understood, not solicited. They receive the right offer when they need it, on the channel they already use. Not ten disconnected reminders, just one relevant one.
Conversion rate on abandoned simulations rises significantly. Credit acquisition cost drops because conversion comes without marketing pressure. Credit market share gains over competitors who chase blindly.
Salespeople work only on qualified, warm leads. No cold calling on stale lists. Sales productivity focused on high-closing-rate conversions.
A prospect arrives on the account-opening or product-subscription path. They fill out the form and upload supporting documents. The orchestration engine validates in parallel: identity (KYC), document compliance (consistency between ID, address proof, bank details), risk profile (AML, sanction lists), engagement capacity if applicable. The prospect receives a reasoned response in minutes: opening validated, additional documents requested with a precise list, or motivated refusal with a possible appeal. No more opaque 72-hour queues.
Multimodal document analysis, KYC and sanction list integration, compliance scoring, journey orchestration, HITL for borderline cases.
The customer perceives a responsive, structured bank that says yes or no clearly. No grey zone for several days. If refused, they know why and what they can do. Sense of respect, not opacity.
Digital path abandonment rate drops. Each abandoned path is a lost customer, often to a neobank. Recovering these paths is recovering customers we would have let go.
The KYC department processes five to ten times more cases with the same team. Agents focus on real risk cases, not on triaging the flow. Workload more evenly distributed, team less stressed.
A customer has not had significant activity for six months. The bank could send a generic reminder, which ends up in spam. Instead, orchestration analyzes: what were they doing before? Which products were they using? Are there recent signals (account check, life event spotted)? It triggers a targeted message on the right channel, at the right time, with real value (contextualized proposal, invitation to a new service relevant to their profile). The customer comes back because they were helped, not chased.
Historical log analysis, weak signal detection, reactivation opportunity scoring, contextual multi-channel orchestration.
The customer keeps the feeling of a useful, not commercial, relationship. When they receive a message, they stop to read it because it is relevant. The bank brand keeps a capital of trust.
Re-engagement rate doubles or triples versus generic campaigns. Reminder budgets are divided by five for an equivalent result. Customer lifetime value extends instead of fading.
Marketing stops spamming its own customers. Less commercial fatigue on the base. Brand image preserved, not worn out by solicitations.
A customer has an unauthorized overdraft following an unexpected debit. They check their app three times in an hour (stress signal), then call support. Before they even tell their story, the advisor sees on their screen the full context: identified disputed transaction, zero overdraft history, ten years of customer seniority. The workflow suggests the right resolution scenario: immediate regularization on commercial terms, no formality. The customer hangs up in five minutes instead of twenty-five.
RAG on internal procedures, complaint criticality scoring, multi-system customer context aggregation, resolution scenario suggestion.
The customer hangs up in five minutes, not twenty-five. They did not have to retell their story to three interlocutors. Their complaint is resolved, not postponed. Their individual NPS goes from detractor to passive, sometimes to promoter.
First-line-resolved complaints do not become costly escalations. The complaint-to-attrition rate drops sharply. Commercial gesture costs decrease because the right gestures are made at the right time, not catching up.
Average complaint handling time divided by two to three. Processing capacity multiplied with constant team size. Supervisors freed from escalations, they can invest in quality.
A banking advisor receives a customer for a wealth meeting. Before the meeting, the AI copilot prepares a brief: asset situation, life events detected (child birth signaled by transactions, recent marriage), tax optimization opportunities identified, comparison with similar profiles. During the meeting, the advisor has natural-language access to all product documentation, internal case law, compliance-certified response scripts. The junior advisor delivers senior-grade quality.
RAG on product documentation and internal case law, customer context aggregation, real-time copilot interface, native compliance.
The customer perceives a bank that knows them, not a counter that changes faces. Advisory quality is constant, independent of turnover. When they come back two years later, their story is not forgotten.
Products-per-customer rate rises because opportunities are seized at the right time. Revenue per advisor increases. High-potential customer retention is stronger because they sense real expertise.
New advisor onboarding goes from nine months to three months for full productivity. Senior advisor departure is no longer a disaster because knowledge stays in the system. Human risk reduced.
The analysis engine spots a bundle of signals: drop in connection frequency, transactions moving to a competitor bank, recent unresolved complaint, search in the customer area for the closing procedure. Rather than discovering the departure three months later in consolidated figures, the bank detects it three weeks early. A workflow triggers the right gesture at the right level: personalized call from the dedicated advisor, negotiated commercial gesture if justified, or simply quality listening to understand. Many departures are calls for help that were not heard.
Attrition prediction model, multi-signal behavioral analysis, criticality scoring, retention path orchestration, HITL for commercial gestures.
The customer who stays feels truly heard. Not a routine end-of-month call, a call because we understood there was a problem. Recognition and trust restored.
Attrition rate drops measurably. For retail banking, keeping a customer costs five to ten times less than acquiring an equivalent new one. Direct savings on acquisition cost.
Sales and advisory teams learn to read attrition signals. Beyond the workflow, a culture transforms: we no longer wait for the loss, we act. Systemic attrition causes are identified and addressed.
Beyond the classic high-revenue segment, orchestration identifies customers with strong loyalty or growth potential: not just rich, but engaged (usage frequency, product diversity, recommendation to relatives, participation in digital events, quality of advisor relationship). It proposes to these customers integration into a structured premium program: banking club with community engagement, exclusive content, physical and digital events, dedicated advisor, tangible benefits adjusted to profile. The program is not fixed: continuous behavioral analysis adjusts engagement, detects key moments (life event, customer anniversary, engagement milestone), and triggers the right gesture to maintain belonging.
Multi-axis behavioral analysis (engagement, equipment, recommendation, seniority), club eligibility scoring, loyalty path orchestration, community management, CRM and event integration.
The club member feels privileged, not solicited. They receive tailored attention, not a status mechanically granted. The bank relationship becomes a pride they share with their entourage. Natural ambassador effect.
Lifetime value of club members rises strongly. Active recommendation rate from this segment carries an organic, high-quality acquisition channel. Strong differentiation versus competitors on the most profitable segments.
The program self-pilots: eligibilities are detected automatically, key moments trigger the right gestures, the member relations team focuses on exceptions and high-value contacts. No manual management of a frozen file.
A customer pays 800 euros at a merchant abroad. The anti-fraud system detects a deviation from the baseline (unusual country, high amount, late hour). Instead of blindly blocking the card, orchestration verifies in parallel: did the customer signal a trip in their CRM? Is there an ongoing hotel reservation in their transactions? Is the overall profile consistent? If yes, silent validation. If no, immediate notification to the customer with a simple question to validate, rather than a brutal block.
Real-time anomaly detection, multi-system cross-reference, HITL framework for borderline cases, multi-channel notification.
The customer does not suffer an unjustified block abroad that leaves them without payment on the evening of a trip. They receive, when necessary, a short clear notification to validate in two seconds. Sense of protection without paranoia.
Real fraud losses drop thanks to better detection of borderline cases. Customer-friction losses (annoyed customers blocked unjustifiably, who switch banks) drop even more.
Anti-fraud teams handle fewer false positives. Agents focus on real investigations. The HITL framework learns from each correction, the model improves continuously.
A customer signs up online for a loan, a life insurance, or opens an account. They upload their supporting documents: ID, bank account details, proof of address, payslips, latest tax return. Before, these documents queued for human review, with a 48 to 72-hour delay. The AI orchestration layer receives each document, analyzes it (advanced OCR, structured extraction, inter-document consistency verification), compares it to KYC references, scores compliance. Compliant cases are validated in minutes. Borderline cases escalate to a KYC agent with full context. Suspicious cases (inconsistency, signs of falsification) trigger an immediate investigation workflow.
Advanced OCR, multimodal image-analysis models, RAG on validation references, existing KYC integration, HITL framework for borderline cases, auditable logging.
The customer receives a validated contract in minutes instead of days. No feeling of waiting in an opaque queue. For insurance: their claim is handled while they are still active, not three weeks later when they have already moved their file elsewhere.
Digital path completion rate rises strongly. Subscription cycle time compresses, generated cash flow faster. For insurance: better post-claim satisfaction, hence retention in life and non-life insurance.
The KYC department processes five to ten times more cases with the same team. Document expertise is automated, human experts focus on real disputed cases. Regulatory compliance strengthened by auditable traceability.
The line between intrusive banking AI and benevolent banking AI is not technical, it is ethical and strategic. Below is the doctrine we systematically apply at our banking and insurance clients to ensure AI strengthens trust rather than erodes it.
All these workflows share one goal: increase the service offered for the customer's comfort. A banking customer who feels tracked — solicited at the wrong moment, blocked without explanation, heard by an advisor who ignores their context — becomes dissatisfied. A customer who feels served — accompanied when needed, protected without friction, welcomed with their history known — recommends. The difference is measured in NPS, in attrition rate, in products per customer. The bank that masters orchestration of these signals in an era saturated with AI is the one that will keep trust.
Architecture compartmentalized by data type. The orchestration engine accesses KYC status without direct access to sensitive supporting documents. Auditable logging of every consultation.
Legal bases documented per use case. Automated right to erasure. Role-based compartmentalization. Banking secrecy requires strict access control that the architecture respects by construction.
Detection of suspicious patterns integrated as an orchestration signal, not as an isolated module. Alerts rise into the HITL framework for human validation on borderline cases.
Banking is classified high risk by the AI Act. Our architecture natively integrates traceability, HITL for critical decisions (credit refusal, account block), model documentation, compliance assessment.
Provided contractually. Documentation, journals, logs and architecture audited by an independent third party at defined frequency.
Beyond orchestration workflows, generative AI opens use cases that were not accessible two years ago. Here are three avenues we explore with our banking and insurance clients.
Once a year, each customer receives a personalized video from the advisor (photorealistic AI avatar of their dedicated advisor) presenting their financial statement: savings growth, projects supported, investment performance, next-year projection. Not a generic PDF. A three-minute video spoken to their name, with their numbers, in their language. Industrializable to tens of thousands of customers without overloading advisors.
Photorealistic Virtual Twin of the advisor, AI text-to-speech with cloned voice, personalized LLM script generation, core banking and CRM data aggregation, multi-language.
For high-end customers (private banking, family office), an AI avatar of the dedicated wealth advisor, available outside business hours. The customer queries their wealth situation in natural language, requests a simulation, prepares a decision. The avatar accesses the documentary RAG (internal case law, taxation, products) and delivers a reasoned response. Critical decisions escalate to the human advisor the next day. The high-potential customer feels their bank serves them when they need, not when the branch is open.
Advisor's Virtual Twin, cloned voice, RAG on wealth documentation, core banking and wealth management integration, HITL for critical decisions.
Banking and insurance advisors produce dozens of documents daily: life insurance quotes, credit simulations, wealth proposals, retirement projections, customer presentations. Generative AI produces these documents in seconds in the bank's brand format, with consistent tone and style, integrating real customer data. The advisor reviews, adjusts, signs. Productivity multiplied, quality homogenized, AI-Act-compliant traceability.
LLM for structured content generation, RAG on compliance-certified templates and clauses, CRM and core banking integration, HITL framework for advisor validation.
One workflow chosen with you (e.g. contextual credit scoring or smart fraud alert), deployed on a customer subsegment. NPS impact measurement and operational delta. Model validation.
Three to four months
Expansion to complementary workflows identified during the pilot. Industrialization of core banking, CRM, fraud integrations. HITL framework rollout. Compliance validated.
Six to nine months
Bank-wide deployment. Connection to all silos. Executive workflow and impact dashboard. Independent audit plan established.
Twelve to eighteen months
Access International orchestrates 9 AI workflows for banking and insurance: cross-sell on life events, digital onboarding, dormant customer reactivation, complaint handling before escalation, advisor equipped with senior copilot, attrition prediction, banking club animation, fraud alert, documentary validation (8 minutes instead of 48 hours). All workflows rely on an orchestration layer that connects to core banking, CRM, risk management, without replacing any existing tool.
Our HITL framework automatically applies the right level of human validation depending on decision type: high-risk credit grant under AI Act, financial advice under MiFID II, AML score under Tracfin obligation, account closure. Each workflow is delivered with its compliance documentation, complete logging for audit, and customer right of appeal. We work in complement with the bank's compliance teams, not as substitution.
Credit scoring and wealth management are the regulated core business of the bank. Our positioning is complementary: we orchestrate data and AI around these activities (file preparation, weak signal alerts, advisor copilot) without substituting for the bank's own decision model. This clear boundary protects our client from regulatory risk and preserves their decision sovereignty.
A banking pilot launches in 8 to 12 weeks: scoping, connection to existing systems (core banking, CRM, risk management), deployment of a priority workflow with its HITL framework, impact measurement on a sample. Extension to 3-4 complementary workflows takes 6 to 9 months. Full industrialization of an orchestration layer takes 12 to 18 months depending on the existing IT system complexity.
Our doctrine is clear: we don't track the customer, we serve them. A bank that uses AI to aggressively solicit a customer in difficulty or hide fees loses trust and the customer. A bank that uses AI to anticipate needs, relieve the advisor of repetitive tasks, and offer service felt as attentive gains in NPS and retention. All our solutions are designed under this grid.
We orchestrate fraud signals from transactions, digital behavior, customer profile, to generate prioritized alerts with uncertainty score. The advisor or fraud team receives the alert with its context: why the system alerted, what actions are recommended, what urgency level. Our HITL framework guarantees that no account closure or extended block is decided without human validation, in compliance with the AI Act.
Yes. Banking and insurance share much of the workflows: digital onboarding, documentary validation, claims handling as a variant of complaint handling, product advice, anti-churn, fraud. Our solutions adapt to insurance specifics: ACPR-EIOPA compliance, IFRS 17, insurance professional secrecy, advisory duty. Our AI documentary validation is particularly effective for insurances handling indemnity files.
We combine LLMs (Claude, GPT, Mistral depending on case), vector databases (pgvector, Pinecone), sourceable RAG frameworks, our proprietary HITL framework, and connectors to major banking systems (core banking, CRM Salesforce or Microsoft, risk management). Model choice is dictated by the bank's sovereignty constraints: Europe hosting, prompt non-reuse guarantee, independent audit.
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