Painpoint

AI for Credit Monitoring

From pilots to implementation

Slyt HQ

31 July 2025

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In a recent McKinsey study, senior credit risk executives from over 20 top financial institutions, including 9 of the 10 largest U.S. banks, were asked where they saw the biggest opportunities for generative AI in credit risk.

While credit decisioning and underwriting are often cited in Gen AI conversations, 58% of respondents believe that portfolio monitoring is the area with the most transformative potential.

Why?

Because portfolio monitoring is where current processes lack efficiency. It’s a core driver of optimization, process efficiency, and early risk detection, all of which are increasingly critical in today’s volatile credit landscape.

Despite recognizing the opportunity, most institutions aren’t there yet. McKinsey found that:

• Only 20% of firms have deployed a Gen AI use case in credit risk
• And 60% plan to do so within the next 12 months

But for portfolio monitoring specifically, AI deployment often stalls at the pilot stage.

McKinsey respondents repeatedly flagged four common blockers:

Lack of real-time visibility into exposures as they evolve

Poor data quality & integration, especially across legacy systems

Insufficient governance to ensure explainability and compliance of AI outputs

Gaps in reusable infrastructure and AI-literate talent

The result? Credit teams spend countless hours stitching together spreadsheets and reports, while missing opportunities to manage risk or optimize capital deployment.

At Slyt, we’ve built our platform specifically around these pain points.

We believe in giving risk and credit teams real-time, AI-powered visibility into their portfolios, with governance and scale built in.

Here’s how Slyt delivers on that promise:

• Real-Time Portfolio Monitoring

Ingest loan-level or program-level data from any source, structure it intelligently, and visualize it instantly. No more chasing down fragmented spreadsheets or waiting for monthly batches.

• Built-In Governance & Explainability

Every AI output comes with audit trails, rationale summaries, and customizable thresholds, so teams can trust and defend what the model says, in line with regulatory expectations.

• Reusable AI Modules

Plug-and-play prompt templates and LLM workflows let credit teams apply generative AI to common tasks, from anomaly detection to risk summaries, without starting from scratch or relying on central IT.

• Designed for Credit Teams, Not Engineers

Slyt is built for the people who manage risk every day, intuitive, secure, and no-code by design. Our mission is to empower the business, not slow it down.

McKinsey’s findings confirm what we hear from credit teams all the time: They want AI that helps them see and act on risk faster.

Slyt was built from day one to make that real.

If you’re ready to move beyond narrow pilots, and build a real-time, intelligent portfolio monitoring engine that scales , we’d love to talk.