GenAI-Assisted Decision Support for Customer Remediation & Case Assessment

Background

Customer remediation programs in financial institutions are designed to identify impacted customers and provide appropriate financial or non-financial remediation. These programs operate under strict regulatory oversight and require a high degree of accuracy, consistency, and explainability.

Remediation analysts are often required to interpret complex eligibility criteria, apply business rules across diverse scenarios, and reference historical remediation outcomes—while managing high case volumes and tight timelines.

The Challenge

Remediation decision-making is inherently complex. Analysts must determine:

• Whether a customer is impacted under defined remediation criteria

• What type of remediation applies (financial or non-financial)

• How to handle edge cases, partial impacts, or ambiguous scenarios

The information needed to make these decisions is typically fragmented across policy documents, remediation logic, historical cases, QA findings, and regulatory guidance. This leads to slower case assessments, inconsistent interpretations, and heavy reliance on experienced subject matter experts.

The GenAI Enablement Approach

Rather than automating remediation decisions, a GenAI-assisted decision support layer is introduced to enable analysts during case assessment.

The role of GenAI is to:

• Analyze approved remediation policies and eligibility criteria

• Summarize relevant business rules for a given customer scenario

• Surface similar historical cases and their outcomes

• Highlight potential risk indicators, exceptions, or ambiguity

GenAI consolidates knowledge and accelerates understanding, allowing analysts to focus on judgment and decision quality rather than manual information discovery.

Human-in-the-Loop Decisioning

All remediation decisions remain fully owned by human analysts.

Analysts are responsible for:

• Reviewing and validating GenAI-generated insights

• Applying contextual judgment based on case specifics

• Determining final remediation outcomes

• Escalating complex or high-risk cases to SMEs or governance forums

This ensures accountability, regulatory alignment, and transparent decision ownership.

Data & Knowledge Sources

The GenAI assistant is grounded exclusively in curated and approved sources, including:

• Remediation policy and eligibility documentation

• Business rules and decision logic

• Historical remediation cases and outcomes

• QA findings, audit feedback, and regulatory guidance

All sources are version-controlled, access-restricted, and regularly reviewed to ensure accuracy and relevance.

Governance & Guardrails

To ensure safe, compliant, and responsible usage, the solution operates within strict governance guardrails:

Advisory-Only Outputs

GenAI provides insights and contextual guidance but does not approve, reject, or prescribe remediation actions. All outputs are non-directive and support analyst judgment rather than replacing it.

Source Attribution

Every GenAI insight is accompanied by clear references to underlying policies, rules, or historical cases, enabling analysts to verify, validate, and challenge outputs as needed.

Confidence & Ambiguity Indicators

GenAI explicitly highlights uncertainty when guidance is ambiguous, data is limited, or historical precedents are sparse. This helps analysts identify scenarios that require deeper review or escalation.

Mandatory Analyst Validation

No remediation action can proceed without explicit analyst review and validation. Human confirmation is required before any outcome is finalized, ensuring accountability and control.

Audit Traceability

Each remediation case maintains a complete audit trail linking GenAI insights, source references, analyst decisions, overrides, and approvals. This enables reproducibility, audit review, and regulatory defensibility.

Business Impact

• Improved consistency in remediation decisioning

• Faster case assessment and reduced backlog

• Reduced dependency on a small pool of senior SMEs

• Enhanced audit readiness and explainability

• Lower rework driven by interpretation errors

Why This Matters

This use case demonstrates how GenAI can be applied responsibly in regulated environments—not by automating decisions, but by enabling analysts to make better, faster, and more consistent judgments.

It reflects a shift from automation-first thinking to AI enablement by design, where governance, transparency, and human accountability are foundational.