Leveraging Generative AI for Financial Analysis

Looking ahead, Generative AI is poised to revolutionize core operations and reshape business partnering within the finance sector. Furthermore, it is anticipated to collaborate with traditional AI forecasting tools to enhance the capacity and efficiency of finance functions. Generative AI in finance has become a valuable tool of innovation in the sector, offering advantages that redefine how financial operations are conducted and services are delivered. Adopting GenAI will help banks realise these objectives through various use cases. In this report we touch base on these scenarios, their benefits and primary risks.

KPMG people combine deep industry experience with extensive technology capabilities to help you achieve your organization’s goals. With 27 years of experience in leadership roles, Unnikrishnan is responsible for developing technology strategy and driving co-innovation with TCS customers. Here, he highlights the transformative potential of AI across various financial services. Generative AI has the potential to transform AML and BSA programs by automating complex tasks, improving detection capabilities, and enhancing regulatory compliance. Despite the challenges of transparency, governance, and data privacy, the integration of AI offers substantial benefits in terms of operational efficiency and regulatory compliance. Financial institutions must continue to innovate and adapt to leverage the full potential of AI, ensuring that their compliance programs remain robust, transparent, and effective in addressing evolving regulatory requirements.

The future of financial services lies in the effective integration of AI, and institutions must act now to harness its benefits and stay competitive in a rapidly evolving regulatory landscape. Generative AI supports IT development by automating coding tasks, generating code snippets, and assisting in quality assurance processes. Additionally, AI plays a crucial role in modernizing legacy systems, enabling them to support advanced applications and meet evolving business needs. AI is transforming customer service through chatbots and virtual assistants, providing personalized and efficient client engagement. These AI systems can handle a wide array of queries, from account information to complex financial advice. RAG implementations involve combining LLMs with external data sources to enhance their knowledge and decision-making capabilities.

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You can foun additiona information about ai customer service and artificial intelligence and NLP. For example, Deloitte’s Trustworthy AI™ framework includes a series of guiding principles to ensure GenAI trustworthiness and reliability. While Gen AI has sparked renewed interest in the potential of artificial intelligence, analytics has been mainstream in the BFSI sector for some time and will continue to play a crucial role. The step forward that Gen AI promises lies primarily in process efficiency and personalisation – areas where decision-making involves numerous data points. LLMs play a crucial role in risk management by analyzing transaction patterns, identifying suspicious activities, and generating alerts for potential compliance violations. This enhances the institution’s ability to detect and respond to financial crimes swiftly. Financial institutions must stay informed about changes in data privacy regulations and adapt their AI strategies accordingly to ensure compliance.

In this article, we’ll get into how GenAI works and how it can impact finance processes. We’ll also explain how our first-of-its-kind and first-to-market AI assistant for CPM, Ask AI, augments daily CPM tasks. AI is changing the face of financial planning and analysis, offering new opportunities for efficiency, insight, and competitive advantage. To fully realize these benefits, it is imperative that finance professionals develop the skills and knowledge to work effectively with AI tools. This requires an investment in learning and development programs that cover not only the technical aspects of AI but also the ethical and compliance considerations.

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“Composable architecture allows payment firms to shorten innovation cycles and improve time to market with an agile and configurable tech stack,” says Jeroen Hölscher, Global Head of Payments Services at Capgemini. A recent McKinsey report found that 90% of financial institutions have, to some degree, a centralised Gen AI function in their infrastructure as of March 2024. Our team can help you dight and create an advertising campaign, in print and digital, on this website and in print magazine. KPMG combines our multi-disciplinary approach with deep, practical industry knowledge to help clients meet challenges and respond to opportunities. KPMG firms are excited about AI’s opportunities and equally committed to deploying the technology in a way that is responsible, trustworthy, safe and free from bias.

The need to drive AI innovation has not always been for the sake of innovation alone. Strong macroeconomic headwinds have made life tough for businesses, particularly SMEs. So by providing more efficient and simpler ways for their customers to pay, these businesses have been able to eke out additional revenue they otherwise may not have.

GenAI is proving instrumental in making digital agents, colloquially known as chatbots, more personal as well. Today’s GenAI-powered agents are summarizing conversations intelligently, offering similarly conversational responses, acting with human-like empathy, and answering an increasingly complex ChatGPT range of customer requests. The result has been reduced customer wait times, and less need for human intervention as digital agents learn how to answer more, and more complex, questions. Pradeep adds that future AI implementations will increasingly focus on innovation and revenue growth.

“In our recent survey, we found that as many as 88% of BFSI pace setter companies – those that enjoy greater financial success than the others – are more focused on using AI to spur innovation,” he notes. As AI capabilities continue to advance, much has been said about the future role of human workers in the BFSI sector. However, as the executives highlight, AI is set to augment, rather than replace, human capabilities. AI technology is capable of examining data and providing valuable insights in minutes that would take humans several hours to figure out. Moreover, Experian data has indicated that 98 per cent of both demographics have had positive experiences with artificial intelligence programs overall.

With our extensive experience in developing AI-driven solutions, we design and implement custom Generative AI solutions tailored to the unique needs of each finance project. Our approach allows businesses to leverage generative AI in business applications, streamlining complex processes and generating innovative content automatically. This technology not only boosts productivity but also enhances decision-making, providing a competitive edge in today’s fast-paced market environment.

• Success in GenAI requires future-back planning to set the vision and a programmatic approach to use-case prioritization, risk management and governance.
• As banks continue on this journey, they can look forward to a more innovative and resilient future, with GenAI as a core component of their digital strategy.
• This includes documenting decision-making processes, conducting regular audits, and maintaining transparency in AI-driven outcomes.
• Additionally, financial institutions need to prepare their workforce for AI integration, addressing potential job displacement concerns and reskilling needs.
• This integrated collaboration between humans and technology could lead to a seismic shift in work culture, maximising productivity and granting the invaluable gift of time.

By leveraging Microsoft’s Azure infrastructure, we ensure that all data used by our AI models is protected and remains confidential. Artificial intelligence has actually been part of the financial industry for quite some time. Early applications included optical character recognition (OCR) for digitizing paper documents and basic machine learning algorithms for financial forecasting.

Data intelligence as the foundation for organisational success

As generative AI continues to evolve, it will undoubtedly bring both opportunities and challenges to the finance industry. The most successful organizations will be those that can effectively harness AI’s capabilities while maintaining a human-centric approach to finance. The future of finance is not about AI gen ai in finance versus humans, but rather about finding the optimal synergy between artificial and human intelligence to create a more efficient, inclusive and robust financial ecosystem. Also, while AI can automate and streamline many processes, it should not have the final say in critical decisions such as loan approvals.

• It can be utilized to analyze customer sentiment, generate personalized financial advice, and automate investment strategies.
• This transformation is not merely a change in daily activities but a leap towards a more impactful and strategic role within organisations.
• Evolving regulations create uncertainty about compliance requirements and the liability risks banks could face.
• The trucking industry uses AI for driver assistance and accident prevention systems, route planning, predictive maintenance and more advanced driver training systems.
• By combining structured financial data with unstructured data from various sources, we’ve been able to provide more comprehensive and accurate analyses.

Muhammad Wahdy, portfolio manager at San Francisco hedge fund Wahdy Capital, offered a compelling argument for why AI won’t quickly replace equity analysts. “I think that right now, AI is not super helpful for portfolio management and equity research. In order to do so, please follow the posting rules in our site’s Terms of Service.

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Once a client is on board, there’s still the matter of understanding and managing their assets, and identifying the best opportunities for their particular portfolio – an increasingly challenging task as asset classes expand and become more complex. Yet today’s consumers, investors, and corporate customers expect a fast and smooth onboarding experience, plus the best advice and asset management available, quickly. Meanwhile, costs for financial organizations are increasing, while profits from traditional income sources are down. Unnikrishnan, meanwhile, highlights the need for specific controls to secure customers’ personally identifiable information (PII) based on geography-specific regulatory mandates.

By processing and interpreting this data in real-time, GenAI can identify patterns and correlations that might be missed by human analysts. This ability to rapidly digest and synthesize information means that traders can respond to market-moving news events almost instantaneously, capitalizing on opportunities or mitigating risks as they arise. One European neobank, bunq, is already using generative AI to help improve the training speed of its automated transaction monitoring system that detects fraud and money laundering. Increased efficiency, and reducing operating costs, is perhaps GenAI’s most well-known benefit. The call to action emphasizes the need for financial institutions to adopt AI technologies proactively, leveraging their potential to enhance compliance and operational efficiency. By embracing AI, financial institutions can improve their ability to meet regulatory demands, deliver superior customer experiences, and drive innovation in their operations.

There might also be a time when it becomes accepted for students to use ChatGPT to aid with schoolwork. Teacher sentiments range from being worried about the technology replacing them to insisting that the in-person classroom connection is essential to education. For example, Microsoft 365 Copilot — a collection of AI-powered tools integrated into Microsoft’s productivity suite — could radically increase office workers’ ChatGPT App productivity. A June 2023 McKinsey report stated that generative AI (GenAI) would automate 60% to 70% of employee workloads. Ricard said much of the potential of genAI depends on humans who could add their own expertise by interpreting and interacting with it. The reach of Gen AI is boundless; it has even helped Klarna save US$10m in marketing costs with its quick ability to run marketing campaigns and generate images.

Further, self-service analytics, made possible by AI, empowers non-financial managers to access and analyze financial data independently, fostering data-driven decision-making across the organization. Existing generative AI technology can already be applied to several areas of Financial Planning & Analysis (FP&A). Daily tasks like financial ratio analysis and financial statement analysis, variance analysis, and reporting can be completed in a fraction of the time using tools like OpenAI’s Data Analyst tool to provide insights into a company’s financial health. AI is also transforming financial review processes, enabling more efficient monthly and quarterly reviews through automated horizontal and vertical analysis. NTT DATA is a global banking innovator and was recently recognized as a Leader in Everest Group’s Open Banking IT Services PEAK Matrix® Assessment 2024. Using trained models on customer trends and data will enable identification of receivables that need prioritisation and improve debt and days outstanding.

While 63% of all respondents reported some familiarity with generative AI, Gen Z and millennials are leading the way in its adoption. Nationally, 47% of respondents said they either use or are considering using AI-powered tools for financial management. For instance, financial analysts can now generate detailed credit risk assessments in a fraction of the time it used to take. By automating data retrieval and initial analysis, these tools free up analysts to focus on more complex and nuanced aspects of their work, ultimately leading to better decision-making.