Generative AI: the next big thing set to revolutionise the banking sector?
The rapid rise of Generative AI (GenAI) has undoubtedly become one of the biggest buzzwords in the tech world today. This cutting-edge technology is poised to transform industries, revolutionise workflows, and redefine how we approach problem-solving. From content creation to decision-making, GenAI is set to leave an indelible mark on the way we live and work. Harnessing the power of deep learning and natural language processing, GenAI systems can generate human-like text, images, and even audio with remarkable accuracy and creativity. This capability opens up a world of possibilities, allowing businesses and individuals to streamline tasks, enhance productivity, and explore new frontiers of innovation. Financial services are changing as a result of generative AI, which presents chances for creativity and efficiency. World is moving towards AI that can be used as a personal banker, providing banking services tailored to customer needs and helping them manage money.
Global banking could gain an estimated $200 billion in value addition from GenAI annually, according to the McKinsey Global Institute (MGI)¹. While banks are in a race to be early adopters of GenAI, lets look into the possibilities where GenAI can revolutionize banking industry.
Proactive Credit Portfolio Management:
Structured data is a challenge to find for most of the Machine Learning implementations and we are the happiest when we get the clean data. Understanding internal customer data to create effective portfolio segmentation and treatment plans is the key to the power of credit portfolio management. Customer portfolios are housed in various systems during initial inventory of portfolio data, which could result in inconsistent information. On the other hand, presence of data gaps allows for improvement in data quality. These results are typical roadblocks in creating a baseline for the portfolio and are components of the preliminary assessment phase.
Generative Adversarial Networks (GANs)² can help with generating near realistic data for credit portfolio management. GANs are capable of producing data that is almost indistinguishable from the genuine thing. With creation of fresh data GANs could be used to train predictive machine learning models and aid in lessening model overfitting.
Automate Regulatory Documents and Policies review:
We know one thing that is, GenAI is good at producing text content. By teaching GenAI to respond to inquiries on rules, corporate policies, and guidelines, businesses are utilising it as a virtual assistant and policy expert. Additionally, it can be used to compare operating methods, rules, and policies. It can send out alerts for possible violations and automate the verification of regulatory compliance.
AWS offers rich document processing pipeline which comprises of three stages: classification, extraction, and enrichment. In the classification stage, AWS offered foundation models (FMs) can now classify documents without any additional training. This means that documents can be categorized even if the model hasn’t seen similar examples before. FMs in the extraction stage normalize date fields and verify addresses and phone numbers, while ensuring consistent formatting. FMs in the enrichment stage allow inference, logical reasoning, and summarization³.
Explainable AI:
Data from financial modelling and trade analysis could be analysed to find trade-related risks and the explanations could be generated using GANs. Customers will be better protected and the company’s financial stability will be enhanced as a result of the bank’s increased ability to manage risk. Think of a loan application denied without explanation, leaving the applicant bewildered and the bank vulnerable to accusations of bias. The customer, seeking answers, demands a reason for the decision.
Srinivasan et al. (2019)⁴ have demonstrated in their research paper a possible application of generating explanations which could be easily understood by various stakeholders for loan denials. It provides reasons to applicants as to why the application was rejected, in a way which is understandable to them.
GenAI Challenges
There are a lot more use-cases that could be solved with adoption of GenAI in banking and these adoptions need to be guided by the regulations set by different Financial Institutes. With Regulatory challenges, it is also crucial to recognise that GenAI is not without its own set of difficulties. One such phenomena that has become a serious concern is hallucination, which is linked to LLMs producing imaginative content that extrapolates from the training data in ways that are unfathomable to humans. It is essential that banks understand the characteristics of various models and choose the one that best suits its needs. When we choose the model which is well trained or fine-tuned on the domain data, it will give us better results when compared to other generic generative models. While implementing GenAI models in banks we also need to make sure that the models are trained on the newest information, not limiting its knowledge of current information and the current world.
Conclusion:
To sum up, the application of GenAI in the banking sector has several advantages that highlight its potential and worth. Its capacity to analyse enormous volumes of data quickly and precisely gives financial institutions the tools they need to successfully manage risks, make well-informed choices, and customise consumer experiences. Banks can improve fraud detection systems, streamline operational procedures, and create individualised financial solutions by utilising artificial intelligence. Moreover, GenAI stimulates creativity by facilitating the development of sophisticated predictive models, opening the door for anticipatory financial planning and flexible response to market swings. Embracing GenAI represents not only a strategic investment in technological advancement but also a commitment to delivering heightened efficiency, security, and satisfaction to both clients and stakeholders in the ever-evolving landscape of modern banking.
References:
- https://www.mckinsey.com/industries/financial-services/our-insights/capturing-the-full-value-of-generative-ai-in-banking
- https://en.wikipedia.org/wiki/Generative_adversarial_network
- https://aws.amazon.com/blogs/machine-learning/enhancing-aws-intelligent-document-processing-with-generative-ai/#:~:text=Generative%20artificial%20intelligence%20(generative%20AI,reducing%20the%20potential%20for%20errors.
- Ramya Srinivasan, Ajay Chander, & Pouya Pezeshkpour. (2019). Generating User-friendly Explanations for Loan Denials using GANs. https://arxiv.org/pdf/1906.10244
- https://github.com/ivorytowerdds/GAN_SDR
- https://github.com/aws-samples/aws-ai-intelligent-document-processing/tree/main/gen-ai
