• Abhishek Bajpayee, BFSI Sales Leader at Hitachi Digital Services, Srinivas Tadepalli, Senior Vice President Global Head of Delivery – BFSI at Hitachi Digital Services


• 19.12.2024 03:15 pm
#ArtificialIntelligence #FinancialServices

The Changing AI Landscape

Organizations increasingly view artificial intelligence (AI) as their long-term strategies. Both service providers and their customers aim to capitalize on the AI revolution. However, a leading industry analyst suggests that only 12% of enterprises are truly prepared for this transformation. Financial services providers are not exempt from the rapid emergence of AI-driven use cases that impact business outcomes. Customers within this sector are particularly interested in leveraging AI to augment and automate critical vertical processes. However, they prioritize initiatives that deliver demonstrable ROI and improve service consistency.

The financial services industry, being heavily regulated, is often slow to adapt to emerging technologies. This cautious approach extends to AI adoption, where any initiative must adhere to rigorous compliance and risk management processes. Yet, AI's integration into risk management is already widespread and growing. According to a survey by NVIDIA, approximately 91% of financial services companies are either exploring or have adopted AI in areas like risk management. Abstract concepts have rapidly evolved into practical tools and accelerators that address specific aspects of the customer journey—both internal and external.

Organizations are reprioritizing discretionary funding to focus on AI initiatives, including the development of proprietary large language models (LLMs). However, this shift sometimes leads to delays or cancellations of planned technology projects. Popular AI use cases across the customer lifecycle—such as customer acquisition, lifetime value management, and risk monitoring—are driving both top-line and bottom-line improvements. For instance, during customer acquisition, AI can generate contextual marketing content, profile leads accurately, and personalize interactions to onboard customers faster, thus realizing value more quickly.

Once customers are onboarded, AI can analyze their preferences and omnichannel behavior to identify cross-sell and upsell opportunities, improving key metrics like revenue per customer. Robo-advisory tools can optimize advisors’ time, while back-office AI agents assist with tasks like document summarization, query responses, and behavioral analysis. Additionally, AI supports intelligent documentation, anomaly detection, and knowledge management, enabling better data quality and operational efficiency.

The Generative AI Challenge for Financial Services

Generative AI has been likened to the advent of computers and even the industrial revolution due to its transformative potential. Financial services leaders are working to understand its implications for their business functions. Some early adopters are already building practices around generative AI, leveraging its capabilities in innovative ways.

One such innovation is the concept of Agentic AI, which enables hybrid models where humans and AI agents collaborate to complete tasks. Use cases include 24/7 customer service desks, risk assessments, and recommendations for financial products. Fintech companies like Betterment and Robinhood (using Akira AI) are already experimenting with such applications. Big players such as JP Morgan, Bloomberg, BNY Melon are taking innovation further.

JP Morgan has been experimenting with synthetic data to develop & train risk evaluation & fraud prevention models. Gen AI helps generate this synthetic data and train the risk evaluation models.

BNY Mellon and Microsoft are joining forces to transform capital markets and the broader financial services industry to explore AI capabilities and provide buy side and sell side clients with a leading data management solution. Bloomberg introduced BloombergGPT, a specialized generative model that is trained on a wide range of financial data to support a diverse set of natural language processing (NLP) tasks.

However, challenges remain, particularly for low fault-tolerance scenarios where LLMs’ probabilistic nature and potential biases raise concerns about transparency and reliability.

Implications for Financial Service Providers – How Far is the New Revolution?

The AI revolution is already reshaping the banking & financial services industry. Banks are experimenting with use cases like fraud management, individualized support bots, and personalized marketing, aiming to enhance employee productivity, operational efficiency, and customer experience. Capital markets are leveraging generative AI for portfolio management, risk assessment, and financial analysis, enabling quicker and more accurate decision-making. While the opportunities to leverage Gen AI in the financial services space are endless, some of the areas focused on value generation in the short term, with adequate internal compliance measures are: content Research for Financial Advisors and RIAs, Personalization & Frictionless Experiences, Investment Decisions and Portfolio Performance, Automated Reporting, Contact Center Enrichment among others.In the payments industry, generative AI applications are improving payment notifications, sentiment analysis, and communication channels, revolutionizing payment management and enhancing customer experiences. Startups are playing a critical role in this transformation. For example:

• Haptik: Offers conversational AI solutions for enterprises, enabling natural and unrestricted interactions.

• Kasisto: Provides KAI-GPT, a banking-specific LLM, for personalized customer engagement.

• Personetics: Delivers real-time financial insights and recommendations based on generative AI.

• Zest AI: Uses AI to improve creditworthiness assessments and lending decisions.

• Trustwise: Provides a single API for safe and responsible AI deployment.

Role of Technology Giants and Hitachi’s Contributions

Leading tech companies like Microsoft, Google, and AWS are advancing AI capabilities in their platforms:

• Microsoft: Offers Azure OpenAI Service for GPT-4 models, Azure Cognitive Search for enhanced data retrieval, and Azure Machine Learning for deploying generative AI models.

• Google Cloud: Provides Vertex AI for unified machine learning workflows and a generative AI API for advanced applications.

• AWS: Features Amazon SageMaker for machine learning and Amazon Bedrock for foundational AI models.

Hitachi is at the forefront of this revolution, establishing a Generative AI Center to address real-world challenges and societal benefits. The Center focuses on improving employee productivity and deriving actionable insights from large datasets. Through its R2O2.ai accelerator, Hitachi supports organizations in adopting reliable, responsible, observable, and optimal AI solutions at scale. Key initiatives include:

• Automating the Confidential Information Memorandum (CIM) process for private equity firms.

• Enhancing customer query response times for insurance clients, reducing turnaround time by over 90%.

• Platform-of-Platforms initiative to support deployment of enterprise-grade artificial intelligence (AI) to address the critical challenges every enterprise can overcome when operationalizing AI and adopting generative AI (GenAI) at scale.

Conclusion

The evolving AI landscape offers immense opportunities for financial services providers to innovate and transform their operations. While challenges remain, particularly around compliance and transparency, the benefits of AI—from operational efficiency to enhanced customer engagement—are too significant to ignore. With strategic investments and partnerships, financial services providers can harness AI’s full potential and lead the industry into a new era of growth and innovation.