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How Pega’s AI-powered case workflow gives fraud investigators a complete, actionable briefing — identifying duplicates, recommending merges, flagging conflicts, and delivering next steps — without opening a single related case.
The Investigator’s Dilemma
A case worker opens a new assignment. Before they can begin working it, one critical question looms: has this already been reported? If it has, working it independently wastes time, creates conflicting resolutions, and risks duplicate outcomes. If it hasn’t, failing to connect it to related cases means missing a pattern — and potentially missing the full picture of what is really happening.
Answering that question used to mean manually searching case history, cross-referencing dates and descriptions, and making judgment calls on incomplete information. With Pega’s Agentic AI, that work is done automatically — and the answer is waiting the moment the case worker opens the case.
What Pega delivers: A structured, AI-generated briefing embedded directly in the case workflow — covering duplicate detection, merge recommendations, conflict identification, pattern analysis, and prioritised next steps — all before the case worker reads a single related record.
The Master Case
Every analysis begins with identifying the master case — the single earliest-filed case by CreateDateTime across all data sources combined. The master case is the surviving reference. It never appears in the duplicate alert. Every other case — including the one currently being worked — is evaluated against it.
This is a critical design decision. The oldest case is the original. Everything filed after it is a potential duplicate, merge candidate, or conflict. Including the current case. There are no exceptions.
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The Three-Way Status Decision
For every related case, Pega’s AI applies a precise three-way status determination. Each status has an unambiguous definition and a specific investigator action:
| Status | Condition | Action |
|---|---|---|
| DUPLICATE | Every attribute matches the master — identical description, FraudType, FraudAmount, FraudPerpetrator, and IncidentLocation. No additional detail exists in this case that is not already in the master. | Close — it is an exact copy. Nothing is lost by closing it. |
| MERGE RECOMMENDED | All attributes match the master but this case contains additional detail — teller notes, caller ID, supplementary evidence — not present in the master. Closing it would destroy investigative value. | Merge into master and explicitly preserve the named additional detail. |
| CONFLICT | Same CustomerID and same incident intent but one or more data attributes differ — FraudAmount, FraudType, IncidentDate, IncidentLocation, or FraudPerpetrator do not match the master. | Retain both cases — list every conflict as master value vs this case value and escalate for investigator decision. |
| NO DUPLICATE | No detection rules triggered. The case shares no meaningful attributes with the master. | Assign as a distinct incident requiring separate investigation. |
The key distinction between DUPLICATE and CONFLICT: A duplicate is an exact copy — close it without hesitation. A conflict is a case where the intent is the same but the data disagrees — that disagreement is itself investigatively significant and must never be silently discarded.
Semantic Interpretation: Understanding Intent, Not Just Text
One of the most powerful aspects of Pega’s Agentic AI is its ability to interpret incident descriptions semantically — understanding what an investigator is describing, not just matching the words they used. This is critical in fraud case workflows because the same incident is routinely described differently across submissions.
Traditional duplicate detection relies on exact text matching — if the wording differs, it misses the duplicate entirely. Pega’s Agentic AI goes further by interpreting the intent behind what is being described, not just the words used. Consider the same event reported through two different channels — one logged by a branch representative, another submitted through a digital self-service portal. The descriptions are completely different in wording, length, and perspective, yet they describe the same underlying event. A keyword-matching system sees two distinct descriptions and creates two separate cases. Pega reads both, extracts the core intent, and recognises them as the same event — flagging the duplicate before it enters the queue.
| Description | |
|---|---|
| Case A | The customer attended the branch and reported that their payment card had been used without their knowledge or consent. Several transactions were identified on the account that the customer did not recognise or authorise. The customer confirmed their card was physically present with them at the time the transactions occurred. The transactions appeared to have been initiated remotely through a card-not-present mechanism. The customer requested an immediate investigation and asked for the transactions to be disputed. A case was opened by the branch representative on the customer’s behalf. |
| Case B | I noticed some charges on my account that I definitely did not make — I have never used my card at these places. My card has been in my wallet the whole time so I have no idea how this happened. It looks like someone got hold of my card details and used them online without me knowing. I tried to call earlier but could not get through so I am submitting this online instead. I want these transactions investigated and reversed as soon as possible. This is the first time something like this has happened to me and I am very concerned. |
| Pega Agentic AI |  Same event — unauthorised card-not-present transactions by an unknown third party. Duplicate detected. |
Semantic similarity is a signal, not a conclusion. Pega always cross-checks structured attributes alongside intent to ensure genuinely distinct cases are never incorrectly flagged.
“The AI has already done the reading — the case worker sees the conclusion, not the raw descriptions.”
Why this matters for investigators: Without semantic interpretation, an investigator reviewing related cases must read every description in full to determine whether two cases describe the same event. With Pega’s Agentic AI, that determination is made automatically — and the investigator sees the conclusion, not the raw descriptions. The briefing tells them what they need to know; the AI has already done the reading.
Business Impact
| Capability |  Without Pega Agentic AI |
With Pega Agentic AI |
|---|---|---|
| Time to identify duplicates | Manual search across case history — minutes to hours per case | Automated at case open — full analysis available in seconds |
| Incident description comparison | Exact string match only — differently worded descriptions of the same event are treated as separate incidents | Semantic interpretation — the AI understands incident intent across different wording, channels, and levels of detail |
| Duplicate identification | Investigator must read and manually compare all related cases — high effort, inconsistent results | Every related case assigned an unambiguous status — DUPLICATE, MERGE RECOMMENDED, CONFLICT, or NO DUPLICATE — with a named reason |
| Merge decisions | Ad hoc — investigator decides based on incomplete information, risking loss of unique case detail | Explicit — merge candidates named with the specific additional detail they contain that must be preserved before closing |
| Conflict visibility | Conflicts silently buried — two cases with the same intent but different amounts or types are worked independently | Every conflict surfaced explicitly as master value vs this case value — investigator knows exactly what to resolve |
| Investigator cognitive load | High — investigator must read every related case to understand the full picture | Low — structured six-section briefing replaces manual review; investigator can act without opening a single related case |
| Duplicate payout risk | High — parallel cases may resolve independently and trigger separate payouts | Eliminated — master case identified, all duplicates flagged before any resolution proceeds |
| Fraud pattern detection | Requires investigator experience and time to spot patterns across related cases | Automated pattern summary delivered at case open — fraud type, method, target, and escalation direction identified immediately |
| Audit traceability | Manual notes — inconsistent, incomplete, dependent on individual investigator practice | Every AI-recommended action logged with the triggering rule, matched attributes, and investigator decision for full regulatory traceability |
Summary
Pega’s AI-powered fraud case workflow transforms the investigator experience from reactive case reading to proactive decision making. By establishing a master case anchor, applying three precise detection rules, assigning unambiguous status to every related case, and surfacing a complete structured briefing at case open, Pega ensures that no investigator ever works a duplicate unknowingly — and no genuine conflict is ever silently discarded.
The three-way status model — DUPLICATE (close it), MERGE RECOMMENDED (preserve the extra detail), CONFLICT (list the discrepancy for human decision) — gives investigators exactly the clarity they need to act with confidence. And because it all happens inside Pega’s unified platform, the gap between AI insight and case action is measured in clicks, not days.
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