Risk & fraud operations

AI for fraud review priorities and investigation follow-up.

See how EkamFlow helps fraud operations teams prioritize unusual activity for investigation and evaluate review quality alongside workload.

Book a demoSee how it works
Decision workspaceIllustrative demo
Business signals
Transaction and account activity historyConfirmed investigation outcomesReview rules, case actions, and available capacity
EkamFlow decisioningPrivate model

Transaction T-6402

Review recommended

Activity patternActivity differs from the recent pattern
Recommended decisionReview the event with established investigation rules
Continue in an existing workflowFraud investigation team

Illustrative workflow and sample data.

Put the decision to work

How does EkamFlow help risk and fraud operations teams?

EkamFlow helps fraud operations teams use transaction history, account activity, and confirmed investigation outcomes to prioritize unusual events for review. A prediction provides an additional signal for an existing investigation process. Analysts combine it with review rules, customer context, and available evidence before determining a next step. Teams can also examine customer activity groups and coordinate follow-up ownership. Evaluation should consider confirmed fraud, false positives, missed events, and review capacity together, rather than optimizing only the number of events flagged.

Evaluation measure 01Confirmed fraud rate
Evaluation measure 02False-positive rate
Evaluation measure 03Review workload

Agree on a baseline before rollout. Measures shown are evaluation criteria, not promised results.

Department workflows

Practical workflows for Risk & fraud operations.

Connect a business challenge to a decision, an action, and an outcome your team can evaluate.

Workflow 01

Prioritize unusual events in a review queue

Review teams must choose which events deserve attention within limited investigation capacity.

Explore fraud detection
Signals
Transaction patterns, account activity, and confirmed investigation outcomes.
AI decision
Rank events showing unusual activity as an additional review signal.
Team action
Combine the signal with established controls and have an investigator review the event.
Measure
Confirmed fraud, false positives, missed events, and review workload.

Workflow 02

Review activity across customer groups

Different customer activity patterns can make a single queue threshold difficult to interpret.

Explore customer segmentation
Signals
Account tenure, transaction activity, engagement, and appropriate customer attributes.
AI decision
Group customers around meaningful activity differences for analysis.
Team action
Use the groups as review context and compare investigation outcomes across them.
Measure
Confirmed events and false positives by group.

Workflow 03

Coordinate the next reviewed customer action

A case may require an investigator, a service owner, or a customer contact step to continue.

Explore next best action
Signals
Account activity, service history, reviewed case context, and permitted follow-up actions.
AI decision
Recommend a relevant next customer follow-up from the agreed action set.
Team action
Have the reviewer apply established case rules and route the next step to its owner.
Measure
Follow-up completion, review outcomes, and customer friction.

Illustrative example

An unusual transaction receives a reviewed next step

  1. A transaction differs from the account's recent activity pattern.
  2. The event becomes a review priority, and an investigator examines the signal alongside established controls and supporting evidence.
  3. The team records the confirmed outcome and any permitted follow-up, then reviews false positives and workload against the baseline.

Start with one decision

What does a useful pilot need?

Select one review queue and agree on the event type and confirmed outcome. Assemble activity and investigation history, identify reviewer capacity, and define how staff review, override, and record the next action.

Considerations for this department

A flagged event is not proof of fraud. Account for the cost of missed events and unnecessary reviews, the time required to confirm outcomes, and effects on legitimate customers. Automated blocking or regulated decisions require separate scoping and validation.

Review EkamFlow data handling

Common questions

Risk & fraud operations questions, answered.

Practical answers about use cases, data, and evaluating a pilot.

How does EkamFlow help fraud review teams?

EkamFlow can use transaction and account patterns to prioritize unusual events for investigation. A reviewer combines the signal with existing controls and evidence, then records the outcome for evaluation.

Does a fraud flag prove that activity is fraudulent?

No. A flag indicates activity that merits review. An investigation determines the outcome. Evaluation should include confirmed fraud, false positives, missed events, and the impact of review on legitimate customers.

Does EkamFlow automatically block transactions?

The workflow described here prioritizes events for human investigation. The business controls how signals combine with its established rules. Automated actions need a separately agreed scope, validation, and review of the relevant controls.

What data supports a fraud operations pilot?

Timestamped transaction and account activity, confirmed investigation outcomes, and existing review context can support a defined pilot. Agree on event types, review capacity, access requirements, and how outcomes will be recorded before starting.

Work across teams

Connect this decision to the wider business.

Agree on customer definitions, action ownership, and outcome feedback across the teams involved. Then adapt the workflow to your industry's data and priorities.

Connected departments

Industry applications

Explore further

Connected customer decisions

Evaluate EkamFlow for a specific business goal.

Discuss the use case, available data, and the next step with the team.

Book a demo