AI Agents

Context

Built for banking within the Stand-alone Quick Win stack and used by Risk teams, this agent focuses on a specific challenge: assessing credit risk for thin-file customers who don’t have enough traditional bureau or internal history to be scored confidently. Instead of defaulting to “no” or relying on blunt heuristics, it augments existing risk processes so these customers can be evaluated more fairly.

What it does

The agent uses alternative data for thin-file customers to complement existing risk views. It takes the standard inputs you already use for credit decisions and enriches them with bank-approved alternative data signals, then produces an additional risk assessment that can be consumed alongside your primary score. The result is not a separate parallel universe of risk, but a structured “second lens” that helps distinguish genuinely risky thin-file applicants from those who are simply under-documented.

Core AI functions

The core AI function is scoring augmentation. The agent focuses on turning alternative data into a usable risk signal and aligning it with your existing scoring approach, rather than replacing core models. It produces a consistent, machine-readable view of risk for thin-file applicants that slots into current decisioning logic and policies, so Risk can treat it as an extra layer of information rather than a one-off experiment.

Problem solved

Thin-file risk assessment is traditionally weak: many customers with limited history are either declined by default or pushed into manual review because standard scores don’t have enough to work with. That constrains growth and creates operational drag, while still leaving pockets of hidden risk. By adding a structured alternative-data risk view, the agent gives banks a way to differentiate within the thin-file segment instead of treating it as a flat “too hard” category.

Business impact

The primary impact is more inclusive lending. Banks can safely say “yes” to a larger share of thin-file customers without relaxing their risk appetite, because decisions are backed by an augmented risk assessment rather than guesswork. For Tier 2–3 banks, this opens up growth in under-served segments while maintaining discipline on expected losses.

Integration and adjacent use cases

Integration complexity is medium: the agent connects to your existing risk and decisioning stack, consumes the alternative data sources you choose to expose, and outputs an additional risk assessment that can be read by your current decision engine and policies—no wholesale rewrite required.

Common combinations in this stack:

• Credit Score Improvement Assistant to recommend concrete actions thin-file customers can take over time to strengthen their credit profile;

• Customer Profiling & Product Recommender to use the richer risk and profile view to suggest suitable products rather than generic offers;

• Customer 360 Summary Generator to give relationship managers a unified view that includes the augmented risk signal for thin-file clients;

• Adverse Media Screening Agent to add news-based risk checks on higher-exposure counterparts identified as promising but thin-file;

• Inbound Email Agent (RM Copilot) to capture and route customer communications related to pre-approvals and follow-ups; and

• Legal Contract Drafting Assistant to generate the appropriate contractual documentation once a thin-file customer is approved under an augmented-risk pathway. Pre-approvals are a natural adjacent use case, where the agent’s alternative risk view supports low-friction pre-approved offers for segments that were previously “invisible” to standard scoring.