Stop policy abuse before it becomes margin loss
Detect early signals of promo abuse, multi-accounting, referral manipulation and first-party misuse – then build rules or models in minutes without adding friction for trusted customers.
.avif)
Trusted by leaders in finance and technology
Policy abuse controls built to protect growth, margin and customer trust
Spot abuse before it scales
Detect weak signals across accounts, devices, referrals, promotions, payments and customer behaviour before abuse becomes a visible loss pattern.
Link behaviour across journeys
Policy abuse is often designed to look legitimate in isolation. Connect activity across customers, accounts, devices and campaigns, and the network behind the behaviour becomes visible.
Adapt policy with evidence
Turn confirmed abuse patterns into rules, models and review playbooks in minutes – with every decision explained and every outcome feeding back into your controls.
One operating layer for abuse detection, investigation and policy control
Policy abuse sits between fraud, growth, customer support and operations. Bring the signals, decisions and feedback loops together and your team can protect incentives, offers and customer experience while keeping genuine customers on low-friction paths.
Protect promotions without limiting genuine customers
Promotions are meant to drive growth. Detect suspicious use of codes, referrals, bonuses and incentives using customer history, device reuse, account links and campaign context.
Detect promo farming, incentive stacking and referral manipulation
Link accounts using shared devices, payment methods, contact details and behavioural patterns
Apply controls at customer, account cluster, campaign or network level
Keep trusted customers on low-friction paths while escalating suspicious activity
Find repeat abusers behind new accounts
Abusers often re-enter through new accounts, new credentials or shared infrastructure. Connect entities across devices, sessions, accounts, addresses and payment instruments, and repeat behaviour surfaces earlier.
Link customers, accounts, devices, sessions and payment methods into one view
Surface repeat users, coordinated groups and account clusters
Detect behaviour that looks legitimate in one account but suspicious across a network
Use connected entity views to support review, enforcement and rule design
Turn abuse patterns into live controls in minutes
When a new abuse pattern appears, teams need to respond without slowing every customer journey. You can build, test and deploy rules or models quickly, using your own policy context and historical outcomes.
Build rules or models from confirmed policy abuse patterns in minutes
Backtest changes against historical campaigns, accounts, claims and outcomes
Tune treatments by campaign, product, channel, customer segment or risk pattern
Version every change so teams can compare, roll back and explain decisions
Improve policy enforcement using real outcomes
Policy controls should learn from what happens after each decision. Approvals, reviews, restrictions, complaints, losses and confirmed abuse all connect back to the controls that shaped them.
Measure which rules and models reduce abuse without suppressing genuine growth
See where policies are too broad, too permissive or losing precision
Feed review and enforcement outcomes back into future decisions
Maintain a clear audit trail from signal to decision to outcome
Identify new bonus abuse patterns before they repeat
Fortify agents help teams spot emerging patterns of attack against new customer bonuses, referral offers and promotional incentives – then automatically search for those same patterns across current activity. Teams stay in control of thresholds, evidence, policy rules and deployment.
Detect repeated abuse patterns across bonus, referral and promotion journeys
Search current customer, account, device and payment activity for matching signals
Summarise the evidence behind high-risk customers, account clusters or campaigns
Recommend rules or tuning actions so confirmed patterns can be monitored again automatically
Prepare evidence for internal review, enforcement decisions and governance packs
.avif)
Add smarter abuse controls to your existing customer journeys
Implementation led by experts
Works alongside your fraud, growth, operations and customer support teams to map policy journeys, decision points, data signals and review workflows – then gets controls live inside your operating model.
Built for measurable impact
Track abuse losses, campaign leakage, review volumes, enforcement outcomes and customer friction from day one.
Runs on your data
Works with your existing customer, account, payment, campaign and behavioural data so you can strengthen policy decisions without losing control of your infrastructure.
Revenue protection
Customer experience
Policy evidence
Based on results from Fortify customer deployments
.avif)
How it works
PROMO, REFERRAL, ACCOUNT OR POLICY EVENT OCCURS
CUSTOMER, DEVICE, ACCOUNT AND BEHAVIOURAL SIGNALS ARE ASSESSED
FORTIFY APPLIES THE RIGHT ACTION: ALLOW, STEP UP, REVIEW OR RESTRICT
TEAMS BUILD OR UPDATE RULES AND MODELS IN MINUTES
Every policy decision becomes part of a stronger abuse control system – helping you detect risk earlier, protect growth and keep genuine customers moving.
One modular system for fraud and AML, built around how teams actually work
Related articles
Regulatory guidance and industry context for financial crime professionals.
Protect incentives, margin and customer trust
Detect repeat abuse patterns, investigate linked behaviour and turn confirmed policy misuse into governed controls, without slowing genuine customers down.
