Maximizing compliance: Integrating gen AI into the financial regulatory framework

You’ve heard it before, but it bears repeating that the potential applications of GenAI in finance are many and continually evolving. Future developments may include more sophisticated AI-driven risk assessment tools, enhanced customer service applications, and even more integrated AI systems that can handle complex financial modeling and scenario analysis. From automating routine tasks to enabling more sophisticated analyses, GenAI is poised to become an indispensable ally in our professional toolkit.

As we stand on the cusp of this transformative era, it is the symbiotic relationship between humans and AI that will define the future of work in finance. The key to unlocking this potential lies in our ability to embrace change, foster innovation, and cultivate a culture of continuous learning and adaptation. At the heart of Gen AI’s potential lies its ability to revolutionise data analysis and problem solving. By harnessing deep learning, GenAI can navigate complex data structures and interpret information with a level of naturalness comparable to that of the human mind. This capability transforms raw data into comprehensible narratives, enabling finance teams to make sense of vast amounts of information and derive actionable insights. But with generative AI proving invaluable for even the most regulated industries, financial institutions now have the opportunity to maximise the value of their data to improve internal processes and evolve customer experiences.

At VentureBeat Transform 2024, attendees will have the opportunity to dive deep into these issues with executives from major financial institutions and tech companies. From exploring the latest AI applications in finance to addressing concerns about job displacement and regulatory challenges, the event promises to shed light on the complex landscape of AI in finance. Don’t miss this chance to be part of the conversation shaping the future of the industry. Generative AI (GenAI), with its transformative capabilities, presents a unique opportunity to drive innovation, streamline operations, and navigate the ever-evolving regulatory landscape. According to Broadridge’s 2024 Digital Transformation and Next-Gen Tech Study, 45% of financial firms allow staff to use GenAI tools for work purposes, and another quarter are training staff on how to use them. It’s where the productivity gains get to a point where you can start to do things you never thought possible.

Experimentation and innovation are critical

Financial institutions must implement robust systems to identify suspicious activities, conduct thorough customer due diligence, and maintain detailed records. The integration of generative AI into these systems can enhance their effectiveness by providing real-time analysis, improving detection capabilities, and streamlining compliance workflows. Generative AI and finance converge to offer tailored financial advice, leveraging advanced algorithms and data analytics to provide personalized recommendations and insights to individuals and businesses. This tailored approach of generative AI finance enhances customer satisfaction and helps individuals make informed decisions about investments, savings, and financial planning.

Regulatory hurdles also pose a major obstacle, with existing laws struggling to keep pace with technological advancements. The complexity of AI models presents challenges in terms of transparency and interpretability, making it difficult for financial institutions to ensure the accountability of AI-driven decisions. There’s also the risk of AI hallucinations or inaccurate outputs, which could have severe consequences for financial operations. Additionally, there’s a significant skills gap, with many finance professionals lacking the necessary expertise to effectively implement and manage AI systems.

How embedded finance and AI impact the lending sector

Moody’s is also exploring AI integration across various platforms, including tools for portfolio monitoring and custom alerts, further enhancing AI’s utility in finance. One of our flagship innovations is Moody’s gen ai in finance Research Assistant, launched in collaboration with Microsoft’s secure Azure environment. This tool uses RAG to ensure responses are grounded in supportable data, mitigating the risk of hallucinations.

FinTech Magazine connects the leading FinTech, Finserv, and Banking executives of the world’s largest and fastest growing brands. Our platform serves as a digital hub for connecting industry leaders, covering a wide range of services including media and advertising, events, research reports, demand generation, information, and data services. With our comprehensive approach, we strive ChatGPT to provide timely and valuable insights into best practices, fostering innovation and collaboration within the FinTech community. The report also dwells on how Generative AI for financial services can enhance enterprise and finance workflows by introducing contextual awareness and human-like decision-making capabilities, potentially revolutionizing traditional work processes.

With genAI and a host of other complementary technologies applied, one could theoretically start to run a continuous close. Hook some visualization tools up to that data, and CEOs and decision-makers could tap into a real-time dashboard of key financial, compliance, risk and cost metrics, for example. Now, they see genAI emerging and are asking themselves (and the rest of the business) how this new and disruptive technology might change their world for the better. This, in turn, requires explainability, or in other words, the ability to understand how GenAI arrived at its recommendations, and what inputs and data the technology drew on to do so.

Today, the adoption of AI in the BFSI sector is being driven by two primary forces. As Babu Unnikrishnan, Chief Technology Officer for BFSI Americas at TCS, explains, the main drivers for AI adoption among BFSI firms are enhancing customer experience and innovation, as well as optimising cost and operational efficiencies. Approximately 77 per cent of surveyed individuals reported using AI tech for finance management tasks at least once a week. Additionally, 60 per cent said AI models can help with budgeting and 48 per cent reported that they were beneficial for investing advice and improving their credit score. AI contributes to IT development by assisting in software development processes, from coding to quality assurance.

In the data collection phase, gather financial data comprehensively from various sources. Next, meticulously cleanse and preprocess the data to remove errors and standardize formats. Augment the dataset with additional relevant features to enhance its richness and diversity. Goldman Sachs, renowned for its prowess in investment banking and asset management, has embraced the transformative potential of AI and machine learning technologies, including Generative AI.

You can foun additiona information about ai customer service and artificial intelligence and NLP. Our community is about connecting people through open and thoughtful conversations. We want our readers to share their views and exchange ideas and facts in a safe space. The industry’s AI spend is projected to rise from $35 billion in 2023 to $97 billion by 2027, which represents a compound annual growth rate of 29%.

Innovations in machine learning and the cloud, coupled with the viral popularity of publicly released applications, have propelled Generative AI into the zeitgeist. Generative AI is part of the new class of AI technologies that are underpinned by what is called a foundation model or large language model. These large language models are pre-trained on vast amounts of data and computation to perform what is called a prediction task. For Generative AI, this translates to tools that create original content modalities (e.g., text, images, audio, code, voice, video) that would have previously taken human skill and expertise to create.

The first is the implementation costs — building out new apps, training them, integrating them into existing systems, testing them, putting them into production and so on. That all takes massive amounts of computing power, loads of data and access to highly skilled people. Centers of excellence may help balance that cost in the initial phases but will likely slow adoption in the long run. When ChatGPT launched in late November 2022, it took just five days to attract 1 million users. And by January it was estimated to have reached 100 million monthly active users.1 Bankers poured back into the office with dreams of massive productivity improvements and — perhaps — a bit more free time.

One of the biggest and most ubiquitous challenges confronting financial service firms is the matter of rising customer expectations. Today’s consumers demand more personalized experiences, higher quality information, and faster responses. Compounding this, traditional organizations are battling new and more nimble competitors, including robot advisors and digital-first trading platforms, that can meet rising consumer demands and offer results with greater efficiency. Chances are, the last time you dealt with your financial institution, artificial intelligence was already involved. You may have had a question answered by a digital assistant, or received a personalized marketing offer, or even been the beneficiary of rapid market analysis.

LLMs can exhibit unpredictable behaviors, especially when exposed to novel inputs. This unpredictability can pose risks in compliance scenarios where consistent and reliable outputs are essential. VAEs are neural network architectures that learn to encode and decode high-dimensional data, such as images or text.

They do this by providing real-time insights and personalized customer interactions. Unlike traditional chatbots, these assistants leverage generative AI and natural language processing. This has become a top priority, as it directly impacts customer satisfaction, loyalty, and ultimately, the success of the institution itself. Currently, there is a growing need among Indian banks to utilize Gen AI-powered virtual agents to handle customer inquiries. Adding Gen AI to existing processes helps banks convert customer call to data, search knowledge repositories, integrate with pricing engine for quotations, generate prompt engineering, and provide real-time audio response to customers.

• As a first step, banks should establish guidelines and controls around employee usage of existing, publicly available GenAI tools and models.
• Other tools — such as Dall-E and Midjourney — also create realistic looking images and detailed artistic renderings from a text prompt.
• With generative AI, finance leaders can automate repetitive tasks, improve decision-making and drive efficiencies that were previously unimaginable.
• The tech adoption strategy of most incumbents involves adding it on top of existing products or using the new technology to boost productivity.

It is transforming from rules-based models to foundational data-driven and language models. With a foundation model focused on predictions and patterns, the new AI can empower humans with advanced technological capabilities that will transform how business is done. These tools include everything from intelligent automation ChatGPT App to machine learning, natural language processing, and Generative AI, and they present new opportunities, possible benefits, and many emerging risks for finance and accounting. Beyond the AI learning initiative, the companies also plan to enhance AI development and benchmarking throughout the financial services industry.

In the GCC, enthusiasm is even higher with two thirds expecting revenue increases and a similar number expecting profitability increases. While these statistics cover various industries, the banking sector specifically has been heavily reliant on technology since its inception. Maufe said that many gen AI deployments in financial services are for internal use cases where organizations are using a human in the loop as a control point. He does however see a near-term future where gen AI is even more widespread and prominent in financial services. AI assistants are the latest tech innovation dominating software in every genre, from ecommerce to project management, scheduling, and home management. It was only a matter of time before they would explode onto the finance software scene.

Surveys that report 54% of roles in banking are at risk of job displacement don’t help either. Just as the steam engine powered the industrial revolution, and the internet ushered in the age of information, AI may commoditize human intelligence. Finance, a data rich industry with clients adopting AI at pace, will be at the forefront of change.

Generative AI algorithms can analyze diverse data sources, including credit history, financial statements, and economic indicators, to assess credit risk for individual borrowers or businesses. This enables lenders to make more accurate and informed decisions regarding loan approvals, interest rates, and credit limits, ultimately minimizing default risks and optimizing loan portfolios. GenAI  offers tremendous potential for enhancing efficiency, personalisation, and customer engagement in the banking sector. However, it also introduces new cybersecurity risks that must be carefully managed. To mitigate these risks, banks need to implement additional security measures, particularly in securing data, ensuring its accuracy and completeness, and maintaining service availability. As a first step, banks should establish guidelines and controls around employee usage of existing, publicly available GenAI tools and models.

Gen AI is now catalyzing a significant shift, with 78% of surveyed financial institutions implementing or planning Gen AI integration. Around 61% anticipate a profound impact on the value chain, enhancing efficiency and responsiveness. Globally, institutions foresee a 5 to 10 year timeline for full automation harnessing, strategically investing in areas with immediate benefits, such as customer service and cost reduction. As the corporate finance landscape continues to evolve, finance leaders and professionals alike are increasingly recognizing the importance of upskilling to work effectively with AI technologies.

While using AI applications, data privacy and compliance with regulatory requirements are crucial for maintaining customer trust and meeting industry standards. In today’s landscape, GenAI represents a paradigm shift in how financial services can be delivered and managed. Its applications range from automating routine tasks to providing deep insights through data analysis, enabling organizations to make more informed decisions, quickly. As per the recent EY report titled “Is Generative AI beginning to deliver on its promise in India? ” 78% of surveyed financial institutions are already implementing or planning Gen AI integration and around 61% anticipate a profound impact on the value chain, enhancing efficiency and responsiveness.