AI-Powered Search for Banking, Insurance & Asset Management

Sinequa connects to all relevant customer data systems — core banking platforms, CRM systems, insurance policy databases, transaction histories, email archives, call center records, and online interaction logs — and indexes them into a unified, searchable layer. When an employee queries a customer, Sinequa surfaces all relevant information from all connected systems simultaneously, presenting it in a role-appropriate view tailored to whether the user is a branch advisor, portfolio manager, compliance officer, or executive. Crédit Agricole implemented this approach across 60,000 internal users, with a projected year-two ROI exceeding $12.1 million from time savings alone.

Sinequa supports financial institutions navigating MiFID II, Know Your Customer (KYC), Anti-Money Laundering (AML), GDPR, and Dodd-Frank compliance requirements. The platform connects compliance teams to all relevant documentation, customer records, transaction histories, and regulatory guidance from a single interface — reducing the manual effort of audit preparation and accelerating responses to regulatory inquiries. For MiFID II specifically, Sinequa enables rapid retrieval of investment advice documentation and trade execution records required for regulatory review.

Standard generative AI tools produce answers based on training data that may be outdated, generic, and entirely disconnected from your institution’s proprietary data. Sinequa’s GenAI assistants are grounded in your enterprise data: they retrieve relevant, current information from your connected systems before generating a response, ensuring that answers are accurate, specific to your organization, and based only on data the requesting user is authorized to see. In a regulated environment where incorrect or inappropriately disclosed information carries compliance and legal risk, this distinction is foundational.

Sinequa’s dynamic relationship mapping connects entities — individuals, bank accounts, credit card numbers, financial transactions, addresses, and communications — across all indexed data sources simultaneously. Machine learning algorithms identify anomalous patterns: unusual transaction networks, connections between accounts that should not be related, and behavioral patterns inconsistent with a customer’s established financial profile. Analysts explore these patterns through interactive charts, timeline analyses, tables, and relationship maps. Because Sinequa searches across all data sources simultaneously rather than analyzing each in isolation, it surfaces money laundering and fraud patterns that siloed analytics would never detect.

Sinequa is deployed across retail banks, investment banks, insurance companies, asset managers, and leasing and property management organizations. Named customers include Crédit Agricole, Société Générale, Groupama, DZ Bank, and Mercer. The platform serves compliance teams for regulatory documentation management, client-facing teams for 360° customer views, investment research teams for cross-source data analysis, and fraud investigation teams for entity relationship mapping across financial transaction data.

Sinequa inherits access controls directly from source systems — ensuring that each user sees only the data they are authorized to access, across all connected platforms simultaneously. Role-based and content-based access control is applied throughout the entire platform. In a regulated financial environment where data access governance is subject to audit, Sinequa’s security architecture means compliance is built into the search layer rather than bolted on after the fact.