Gen AI for Banking: The New Wave of Gen AI to Manage the Credit Value Chain

The banking sector is currently at a turning point. While the last decade was characterized by the digitalization of data, the next will be defined by the ability to interpret and manage the complexity of unstructured content. In an ecosystem where regulatory compliance and decisional speed are the pillars of competitiveness, Generative Artificial Intelligence (GenAI) emerges as the enabling factor for a profound transformation of internal processes.

1. Beyond Automation: GenAI as an Engine for Synthesis and Analysis

Generative AI represents a paradigm shift compared to traditional predictive modeling. While the latter excels at identifying statistical patterns, GenAI introduces the ability to interact with natural language and heterogeneous knowledge bases. For financial institutions, this means finally being able to tackle so-called "human-intensive" processes—activities that require in-depth analysis of diverse documents such as financial statements, appraisals, supplementary notes, and contracts.

The adoption of Large Language Models (LLMs) allows for the automation of the "Document Value Chain", transforming static documents into actionable information flows. Efficiency stems not only from execution speed but from the system's ability to understand causal links and produce accurate summaries, drastically reducing manual workload and the risk of human error, while leaving the operator in the fundamental role of supervisor and final validator.

2. Cases of Excellence in the Global Banking Landscape

The adoption of GenAI is entering banking processes disruptively, moving from a theoretical hypothesis to a consolidated reality in some of the world's largest financial institutions that have integrated it into their core processes to achieve measurable competitive advantages:

• JPMorgan Chase: The bank has implemented a generative AI-based suite used by over 230,000 employees to analyze call transcripts, analyze client company documents, compare complex financial documents, and synthesize massive volumes of data. This system allows analysts to save between 3 and 6 hours of work per week, drastically speeding up research and analysis phases. (Link)
• Morgan Stanley: The institution developed an assistant based on Generative AI to support its financial advisors in gaining rapid access to a repository of over 100,000 research reports. Efficiency in document retrieval increased from 20% to 80%, allowing advisors to dedicate more time to direct client relationships.  (Link)
• HSBC: Uses GenAI to support credit analysis and report writing, accelerating loan decisions. Furthermore, it introduced code assistance tools for 20,000 developers, achieving a 15% efficiency increase in software writing and security. (Link)
• Goldman Sachs: Launched the "GS AI Assistant" to automate repetitive tasks such as summarizing complex documents. Engineers use AI to automatically write and test code, significantly reducing the time-to-market for new digital features. (Link)

3. Mapping High-Value Added Processes

According to analyses conducted by CRIF, the effectiveness of GenAI should not be dispersed in generic applications but focused on critical areas where document volume and regulatory complexity create the greatest bottlenecks. Among the processes identified by CRIF as yielding the greatest benefit:

• Credit Origination/Underwriting: Automation of data collection and analysis for approval.
• Compliance and Anti-Fraud: Reputation screening and anti-money laundering document verification.
• Risk Monitoring: Continuous analysis of weak signals present in corporate documentation.
• Second Level Controls: Systematic review of portfolio quality and adherence to procedures.

4. Analysis of Use Cases and Operational Impacts

Below are some use cases that CRIF has been able to experience directly in the field through strategic collaborations with various banking clients, providing concrete proof of the effectiveness of these solutions in real operational contexts.

• AML and Anti-Fraud Control Automation: One of the greatest burdens for compliance offices is manual investigations to discriminate homonyms or verify reputational alerts (PEP, Sanctions). The solution proposed by CRIF allows shortening of analysis time maintaining high accuracy and ensuring traceable reporting.
• Document Value Chain Optimization: Managing heterogeneous documents (pay slips, notary deeds) often slows down credit processing. Through intelligent workflows, it is possible to transform PDFs into structured information. Most of the documents provided can be processed without human intervention maintaining a high level of accuracy.
• Advanced Financial Assessment: The use of GenAI allows for the reconstruction of causal links in corporate performance by automatically analyzing balance sheets and supplementary notes reducing processing time.
• Second Level Controls and "Agentic Analyst": Traditionally, teams check only 0.5% of cases. The use of AI agents allows extending coverage to 100% of dossiers, flagging anomalies relative to policies. This leads to a major time saving and improving the rate of risk detection.
• Personalized Collection Strategies: By integrating predictive models and GenAI, personalized action plans (installments, settlements) are defined, identifying the best contact channel. Benefits include a significant reduction in operational costs.

Conclusion: The Need for Strategic Selection

In conclusion, Generative Artificial Intelligence proves to be a powerful ally for streamlining banking processes, capable of slashing operational costs and response times. However, to maximize the Return on Investment (ROI) of these pioneering projects, adopting technology indiscriminately is not enough. Accurate analysis is required to identify the processes best suited for intelligent automation, balancing technical complexity and business impact. Only through strategic selection and responsible governance will banks be able to transform the potential of GenAI into a concrete and sustainable competitive advantage.