SOLUTIONS / FRAUD
Fraud Risk & Investigation
Intelligence
Organized fraud costs banks, agencies, and enterprises billions. Siloed controls hide the bigger picture.
Connect fraud signals across your entire program to uncover coordinated activity earlier, close cases faster, and prevent losses.
The challenge
Keep Your Tools. Fix the Gaps.
Can you check for fraud signals across systems, business lines, and data sources at once?
Do your investigations require manual data gathering, multiple handoffs, or even IT support?
Have you already invested in anti-fraud technology, only for fraudsters to quickly find workarounds?
The solution
DataWalk For Connected Anti-Fraud
Step 01
Connect All Fraud Relevant Data
DataWalk brings together previously siloed internal and external data relevant to fraud prevention. Cybersecurity, Customer Service, external registries, and anything else.
Step 02
Reveal the World Behind Rows and Columns
DataWalk organizes data to reflect your operations, resolving real-world objects and revealing the relationships between them.
Step 03
See What Individual Controls Cannot
DataWalk lets you detect, investigate, and monitor networks, fraud rings, and suspicious patterns that individual controls cannot see.
Step 04
Adapt Faster Than the Fraudsters Targeting You
DataWalk lets you quickly adapt to new fraud schemes, changing tactics, and evolving hypotheses.
Proof
The Impact
$10–100M+
in additional fraud prevented
Hours, not weeks
to close complex cases
<20 weeks
to go live
Case study
Always Ahead: Building the Safest Digital Bank in America
DataWalk allowed us to bring all of our data together in one place, standardize it for the first time, and finally run true cross-business-line analysis. Before that, we were completely siloed.
~$20M
in fraudulent attempts identified
Tens of billions
of records analyzed collectively
19 weeks
to go live
Use cases
Addressing Fraud Challenges Across Industries
For Banking
Accounts, applications, payments and the people behind them.
Identify connected activity across accounts, identities, devices, and beneficiaries.
WHAT TO LOOK ACROSS
- Mule accounts
- Stolen or manipulated identities
- Shared devices and beneficiaries
Connect digital activity with the identities, accounts, devices, beneficiaries, and counterparties behind it to uncover account takeover and coordinated fraud.
WHAT TO LOOK ACROSS
- Account takeover and compromised credentials
- Suspicious account and beneficiary changes
- Shared devices, accounts, and counterparties
Reveal connections between applications that appear to come from unrelated borrowers.
WHAT TO LOOK ACROSS
- Synthetic identities
- Manipulated applications
- Reused addresses and devices
Connect employee activity with transactions, counterparties, and external relationships.
WHAT TO LOOK ACROSS
- Unusual access patterns
- Connected-party transactions
- Undisclosed relationships and collusion
For Insurance
Claims, claimants, providers and the assets between them.
Connect claims across people, assets, accounts, and other shared relationships.
WHAT TO LOOK ACROSS
- Shared claimants and beneficiaries
- Reused addresses and accounts
- Connected vehicles and properties
Identify connections between applications, identities, assets, and supporting information.
WHAT TO LOOK ACROSS
- False or manipulated information
- Reused identities and addresses
- Connected applicants and accounts
Identify suspicious relationships and activity across providers, customers, and claims.
WHAT TO LOOK ACROSS
- Repeated provider relationships
- Unusual referral patterns
- Connected payment flows
Connect employee or agent activity with claims, payments, and external parties.
WHAT TO LOOK ACROSS
- Unusual access patterns
- Connected external parties
- Repeated suspicious involvement
For Government
Programs, applicants, vendors and public funds.
Connect applications, recipients, identities, and payments across entire programs.
WHAT TO LOOK ACROSS
- Duplicate or synthetic identities
- Shared contact details
- Common payment destinations
Reveal hidden relationships between bidders, vendors, intermediaries, and officials.
WHAT TO LOOK ACROSS
- Hidden bidder relationships
- Shared ownership and addresses
- Connected vendors and officials
Identify connections between applicants, organizations, owners, and payment destinations.
WHAT TO LOOK ACROSS
- Related applicants
- Common beneficial owners
- Shared payment destinations
Connect internal activity with approvals, payments, and external relationships.
WHAT TO LOOK ACROSS
- Unusual approval patterns
- Connected external organizations
- Undisclosed conflicts
For Enterprises
Vendors, third parties, supply chains and employees.
Reveal hidden relationships between vendors, employees, organizations, and payments.
WHAT TO LOOK ACROSS
- Related or shell vendors
- Employee-vendor relationships
- Connected payment destinations
Connect suppliers, distributors, contractors, and their underlying relationships.
WHAT TO LOOK ACROSS
- Hidden ownership relationships
- Connected counterparties
- Reused contact information
Connect suppliers, shipments, goods, and transactions to uncover suspicious activity across the supply chain.
WHAT TO LOOK ACROSS
- Falsified shipments and documentation
- Connected suppliers and intermediaries
- Irregular goods and payment flows
Connect customers, orders, accounts, devices, addresses, and payments to uncover related activity.
WHAT TO LOOK ACROSS
- Fraudulent accounts and identities
- Shared devices and addresses
- Connected orders and payment methods
Find the Anti-Fraud Intelligence Hiding in Your Data
Stop more fraud by connecting the signals across the systems and controls you already have.
FAQ
Frequently Asked Questions
Find your answer
What is DataWalk's fraud risk and investigation intelligence solution?
DataWalk is an analytics platform that connects fraud-relevant data – including cybersecurity, customer service, and external registry data – across previously siloed systems into a single connected view. It resolves real-world objects such as accounts, identities, and devices and reveals the relationships between them, allowing investigators to detect fraud rings and suspicious patterns that individual controls cannot see on their own.
How does DataWalk detect fraud across different systems and business lines?
DataWalk brings together your internal and external data sources, including cybersecurity, customer service, registry data, and anything else, into one connected model so investigators can analyze activity across business lines instead of within separate silos. DataWalk has enabled banks to standardize their data and run true cross-business-line analysis after previously operating in isolation. This connected view surfaces coordinated activity that single-system controls miss.
How is DataWalk different from traditional anti-fraud systems?
Traditional fraud systems operate in data silos, which hides coordinated fraud activity that spans multiple systems. DataWalk instead organizes data into one ontology and one connected state, running graph, search, and SQL queries through the same engine rather than copying data into separate tools for each workload. This allows teams to detect and investigate fraud rings, synthetic identities, and shared-device patterns that siloed, individual controls cannot reveal.
How long does it take to deploy DataWalk for fraud investigation?
Deployment timelines vary by organization, but DataWalk's case study with Ally Financial reports a go-live time of 19 weeks, during which the bank brought together and standardized data across business lines for cross-business-line fraud analysis.
Which industries does DataWalk support for fraud prevention?
DataWalk supports fraud investigation in banking, insurance, government, and enterprise organizations. In banking it addresses deposit, digital banking, loan, and insider fraud; in insurance it covers claims, application, provider, and insider fraud; in government it covers benefits, procurement, grant, and insider fraud; and for enterprises it addresses procurement, third-party, supply chain, and e-commerce fraud. Each use case connects the identities, accounts, and relationships specific to that fraud type.
How does DataWalk support AI agents in fraud investigations?
DataWalk gives AI agents governed enterprise knowledge and graph computation they can work through step by step, so an agent can inspect a result and then narrow, branch, or change direction during an investigation. This matters because AI inherits the blind spots in the data beneath it: if that data is fragmented, inconsistent, or incomplete, AI automates the same uncertainty investigators already face. DataWalk connects that data so the evidence behind an AI-assisted investigation can still be checked.