AI-powered approaches to reimagining commercial banking

Integrating AI solutions into commercial banking processes helps to streamline operations, enhance data accuracy and improve decision-making across various functions.

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Featured Thinking

Digitalization challenges | AI use cases: commercial credit | Strategic opportunities for banks

Digitalization challenges

As commercial banks work to modernize and scale operations, two major challenges often stand in the way: fragmented, hard-to-access data and overly customized solutions that add complexity without delivering end-to-end efficiency.

Data quality and access:
Unstructured data is often where the value lies, including multiple systems of record creating data silos. This data has not been subject to the rigor of storage and compliance required of structured data.

Bespoke and piecemeal solutions have created complexity:
Iterative solutions make it difficult to achieve overall scale benefits and improving each piece individually cannot deliver the scale of a fully integrated process.

Today, banks are in a race to build their AI capabilities and deploy impactful use cases. They need to enter the race to use AI to transform their business. That’s the first-mover advantage.

Matt Cox

EY Global Corporate, Commercial and SME Banking Consulting Leader

Commercial credit: Unlock the potential of AI

In today’s competitive market, AI is essential for streamlining commercial credit end-to-end processes. From frontline relationship management to back-end operations, AI enhances decision-making, automates routine tasks and unlocks new insights.

Commercial credit AI use cases

Ingest and process client onboarding information to create new records
Process financial statements into spreading templates and adjust to align to credit policy
Generate credit approval memo narrative and recommendations
Determine product pricing based on predictive models and client-specific risk patterns
Recommend leads based on client indicators and common client profiles
Process financial statements into spreading templates and adjust to align to credit policy
Generate credit approval memo narrative and recommendations
Generate suggested terms and covenants based on client risk profile and product type
Automatically book loan or facility upon closure of contract
Automatically book loan or facility upon closure of contract
Verify the collection of all executed legal documents and assess them for adherence to effectiveness criteria
Compare finalized contracts and other source documents to loan booking data to confirm accuracy
Perform automated covenant monitoring, inclusive of documentation request and execution of testing
Ingest corporate actions and process servicing workflow as directed in notice
Generate risk rating based on predictive and historical models
Leverage client intelligence for portfolio analytics and trend prediction
Automate analysis of covenant compliance with recommended next steps for exceptions
Perform large-scale data comparisons across systems to confirm consistency
Generate regulatory reporting based on prompts aligned to requirements
Perform data testing to identify potential anomalies based on business rules
Generate reporting based on prompts aligned to requirements
Leverage operational data and pricing data to determine profitability and recommend pricing or product updates to drive higher profitability
Leverage closing or funding schedules to automate funding source and reduce customer delays and account overdrafts
Generate reporting based on prompts aligned to requirements
Conduct ledger analysis to perform reconciliation tasks and resolve cash discrepancies

Strategic opportunities for banks

Unlocking this potential will require an orchestrated combination of technical and domain expertise. By developing a unified AI platform that connects disparate systems and data, organizations can move beyond isolated agents to create an interconnected network of services. This integrated approach not only enables smoother operations and deeper insights but also lays the foundation for scalable success across a range of use cases.

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The team

Matt Cox

EY Global Corporate, Commercial and SME Banking Consulting Leader

Large-scale transformation leader and strategist focused on Wholesale Credit and Transaction Banking. Husband. Father of two. Nomadic traveler.

Charlotte, NC, United States

James Sankey

EY EMEIA Corporate, Commercial and SME Banking Leader

Focused on helping corporate, commercial and SME banks transform in an ever-changing world. Enjoys helping others. Curious and creative. Passionate about nature and sustainability.

London, United Kingdom

Abhay Chauhan

EY Asia-Pacific Corporate, Commercial and SME Banking (CCSB) Leader

Helping businesses build competencies focused on sustainable growth. Contributing to the development and collaboration efforts in the corporate, commercial and SME banking (CCSB) space.

Singapore, Singapore

Sameer Gupta

EY Americas Financial Services AI Leader

Tech evangelist and communicator. Enjoys cooking, woodworking and astronomy.

New York, NY, United States

Adam Smith

EY Americas Corporate, Commercial and SME AI Banking Lead; Managing Director, Financial Services Consulting, Ernst & Young LLP

Enhancing efficiency across commercial lending by bridging business operations and technology. Empowering processes and interactions through advanced AI capabilities. Father of four. Avid sports fan.

Charlotte, NC, United States

EY refers to the global organization, and may refer to one or more, of the member firms of Ernst & Young Global Limited, each of which is a separate legal entity. Ernst & Young Global Limited, a UK company limited by guarantee, does not provide services to clients.