In their 2024 research report, Generative AI in Risk and Compliance: Friend or Foe?, Parker & Lawrence examine the impacts and risks of emerging AI capabilities across 14 RegTech use cases, with insights from 17 expert contributors.
Among those use cases is credit risk:
Credit Risk Tools
Credit risk tools automate the assessment of debt default risk, including analysis of credit history and financial health. They allow clients to assess their customers’ risk from the outset, with credit scoring and decision models, and throughout the subsequent relationship, with implications for collections, deductions and more.
Credit services span a wide range of industries, from retail banking to investment risk management and corporate credit management. With market and portfolio risk covered in a separate section, our scoring and analysis here reflects our view Generative AI in retail banking and corporate credit management contexts.
Key Generative AI Use Cases in Credit Risk
Expert contributors helped map the opportunities, risks and imminence of 400+ Generative AI applications. The mapping of credit risk opportunities shows 7 impactful Generative AI capabilities:
Creating New Data: The process of using generative AI to produce novel datasets that mimic real-world data structures and patterns, particularly for the purposes of training algorithms or testing system performance.
Labelling Existing Data: The application of generative AI to automatically assign meaningful labels or tags to data, potentially unstructured or semi-structured, enhancing data organisation and usability for machine learning models.
Interpreting & Summarising Information: Utilising Generative AI to digest and condense large volumes of data into concise summaries, providing insights and overviews that aid in decision-making and comprehension.
Creating Reports: The use of Generative AI to create detailed, structured reports based on input data. This includes comprehending the potentially formal and official nature of regulatory reporting.
Editing Reports: Generative AI’s ability to identify errors, refine, correct and otherwise improve reports. This usually involves mapping reports to the policies or regulations which necessitate them, and the input data which inform them, for maximum context.
Client Interactions: The deployment of generative AI in interactive applications such as chatbots or guided workflow tools to simulate human-like conversations, providing support to end-customers.
Internal Co-pilot: Leveraging generative AI to assist in investigative tasks by generating hypotheses, suggesting lines of inquiry, and synthesising findings from diverse data sources to support human investigators.
Impact vs Readiness
Want to dive deeper? Check out the full report for expert commentary:
Key Generative AI Risks Mitigated through Credit Risk Tools
The analysis also accounts for the emerging risks which technology solutions will need to solve. The mapping of credit risk tools finds that they can contribute towards solving 1 major risk:
Bias: The inadvertent reinforcement or creation of prejudiced outcomes by Generative AI. This encompasses any form of partiality, discrimination, or unfair weighting in the information or decisions generated by AI.
Although this is not an imminent concern, if firms start to incorporate Generative AI models in their credit risk decision models, then fears of bias may become a reality with serious impacts on individuals. Using more traditional credit risk software as an overlay in these cases may help to mitigate Generative AI bias, by offering a simpler, more explainable perspective on each customer’s characteristics.
Severity vs Imminence
The Industry's Perspective
Throughout the research, experts from technology vendors, regulated institutions and regulators share new insights on Generative AI in risk and compliance. Thank you to Emagia for contributing an industry perspective on Generative AI in credit risk.
To keep this blog bitesize, here is just one excerpt inspired by Emagia's insights:
The Generative AI Opportunity in Credit Risk
Emagia’s application of GenAI in credit risk exists within their broader Order-to-Cash management platform for B2B customer relationships and Enterprise Resource Planning (ERP). They deploy AI in their credit risk scoring and limit setting models, but use Gia, their Generative AI assistant, to bring their broader suite of modules together. As the digital finance assistant at the centre of their platform, Gia allows clients to seamlessly manage credit risk alongside receivables, collections, deductions and more.
Gia is a self-learning AI, enhancing customer interactions with conversational abilities, personalised communication and financial document handling, including invoices and payment options. Emagia have put Gia to work through various specialised applications:
GiaDocs: This tool leverages AI to streamline document processing tasks, significantly reducing costs by up to 90% and achieving high rates of straight-through processing.
GiaGPT: Designed as a co-pilot for finance leaders, GiaGPT processes financial documents and spreadsheets to provide insights through summaries and visual representations, enhancing operational efficiency.
GiaPay: An enterprise SaaS solution that facilitates B2B payments, GiaPay incorporates Generative AI to interact with customers during the payment process, providing details on invoices, offering payment options, and logging dispute information.
Importantly, Emagia utilises Generative AI not as a direct decision-maker in credit assessments but as a sophisticated means of aggregating and presenting comprehensive data to credit managers, thereby enabling informed decision-making. This approach reflects a shift in the industry perception post-ChatGPT, where the enhanced accessibility and demonstrable capabilities of GenAI have converted scepticism into trust among credit professionals.
Emagia is a leading provider of digital finance solutions, specialising in Order-to-Cash automation. They offer AI-powered analytics and data-driven insights to enhance financial operations. Founded in the early 2000s, Emagia serves businesses worldwide, helping streamline processes and improve financial decision-making and efficiency.
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