Banking & insurance

Banks and insurers each see only a fragment of what criminals, fraudsters and high-risk customers do. The full picture only emerges when signals are combined, yet customer and transaction data may not leave the institution. Linksight enables joint screening, monitoring and model training without data changing hands.

KYC (identity & blacklist screening)

Screen customers against information you could never hold yourself.

KYC requires verifying who a customer is and screening them against sanctions lists, watchlists, and blacklists, at onboarding and in periodic reviews. The most valuable lists belong to other institutions and authorities, and no one may simply hand them over. Someone rejected at one institution no longer gets a clean start at the next, while legitimate customers clear onboarding faster. Every check is provable to regulators, and benchmarking against peers shows compliance teams how their own process performs. Linksight matches customer identities against lists held elsewhere without either side surrendering its data: the screening institution learns only whether there is a hit. Each check is logged for audit.

AML (collaborative transaction monitoring)

See the laundering patterns that spread across banks on purpose.

Money laundering is built to stay invisible: mule networks, smurfing, and layering deliberately spread the money flow across multiple banks. Each bank sees only its own fragment, and its duty of confidentiality to customers keeps it that way. Monitoring together, banks detect networks none of them could see alone, and their reports to regulators and financial intelligence units get sharper. False positives drop, and criminal money finds fewer gaps between institutions. With Linksight, banks analyze transaction patterns jointly while every bank’s transaction data stays within its own walls. Only privacy-preserving signals about suspicious flows cross the boundary.

Cross-organization risk scoring

Weak signals from multiple organizations add up to one reliable risk score, without sharing customer data.

Risk scoring estimates how likely a customer, transaction, or policyholder is to present financial, fraud, or compliance risk. A single organization rarely sees enough to judge well: its own signals look innocent in isolation, and the pattern only appears when they are combined with what others see. When banks and insurers score together, risks surface that no single party could have spotted, and losses on lending, underwriting, and claims go down. Customers get faster, more consistent answers, and investigators spend their time where the real risk sits. Through Linksight, the score is computed over data from all participating organizations while each party’s data stays at its source. Only the resulting scores and insights are shared, never the underlying customer data.

Fraud detection (federated model training)

Fraud models that learn from every bank’s data and see none of it.

Fraud detection models improve when they train on data from multiple banks and insurers rather than one institution’s slice. Privacy regulations and competitive concerns rule out pooling that customer and transaction data. Models trained across institutions catch fraud patterns earlier, before losses mount, and recognize schemes no single dataset contains. Fewer false positives keep investigation teams focused. With Linksight, institutions train fraud models through federated learning: the model learns at each bank while the data never leaves it. What emerges is shared intelligence without shared records.