Numerous organizations in the banking, financial services, and insurance (BFSI) sector are actively exploring artificial intelligence (AI) technologies.
According to the ViaCatalyst 2023 Global Cloud Study, a striking 82% of BFSI participants reported that their investments in artificial intelligence (AI) and machine learning (ML) have grown over the last one to two years. Remarkably, 87% anticipate further investments in AI and ML in the coming one to two years.
With the rise of generative artificial intelligence (GenAI) to prominence, companies have been seeking the most effective strategies to harness its considerable capabilities. BFSI entities, which have always valued analytics but are also tightly controlled by compliance and regulatory requirements, have seen significant impacts from their initial uses of AI.
In recent years, particularly the last four to five, the use of predictive AI has offered an innovative alternative to conventional analytical approaches due to its sophisticated modeling abilities. The emergence of GenAI has broadened these capabilities, offering transformative possibilities. For BFSI companies, the potential benefits are significant and include:
- Enhancing customer experience by improving personalization in advisory services and interactions across various channels and customer segments.
- Advancing sales and service by identifying critical ‘moments of truth’ that sway customer choices and setting apart service levels across different products and offerings.
- Optimizing efficiency by revolutionizing business processes, back-office functions, and regulatory compliance.
- Enriching workforce and workplace efficiencies through the deployment of advanced AI tools.
AI Adoption and Opportunities in BFSI
Our examination of Generative AI (GenAI) projects within BFSI organizations aimed to gauge the level of maturity in adoption and the potential for business improvement. These projects are predominantly in the experimental or planning stages, focusing mainly on two key objectives: enhancing the customer experience and boosting operational efficiency.
Initiatives involving virtual assistants, streamlined processes, and customized services are the most common, with efforts in improving decision-making, detecting fraud, offering automated financial advice, and ensuring compliance not far behind. These efforts are largely experimental, displaying a wide range of maturity in adoption, yet they hold considerable promise for business improvement in areas like process optimization, tailored services, automated financial advising, and regulatory adherence.
We delved deeper into the value chains of banking, capital markets, and insurance sectors to pinpoint specific areas of impact (refer to Table 1). For each sector, the influence of both predictive and generative AI on business outcomes will differ across the spectrum of customer engagement, from initial contact and onboarding to servicing, and from establishing relationships to expansion and regulation, potentially leading to changes, simplifications, or complete overhauls.
The analysis demonstrates AI’s role across the BFSI value chain, from customer engagement to regulatory adherence.
In banking, AI’s transformative power is especially pronounced. As GenAI becomes more widely adopted, banks will be able to integrate AI more thoroughly into broader banking operations and customer interactions. AI-enhanced features, such as highly personalized marketing, discreet Know Your Customer (KYC) processes, and sophisticated needs assessments, will significantly improve customer interactions and satisfaction.
In the capital markets, the integration of AI and GenAI introduces new capabilities like knowledge management, content analysis, summarization, generation, and the creation of synthetic data. These advancements will improve customer interactions and lead to business model transformations.
The insurance sector is also witnessing a digital overhaul in business processes and customer engagements, a trend that will be accelerated by AI adoption. Large Language Models (LLMs) have the capacity to revolutionize the insurance value chain, aiding everyone from agents and brokers to underwriters and claims handlers, with process transformation being a key benefit for providers.
It’s crucial to recognize that in all these areas of impact, smart technologies serve as an aid to human workers, not as their replacement. AI will support people in their everyday tasks, enabling them to make more informed decisions and innovate in ways that can transform their entire organization.
Our GenAI Vision – The Continuum
AI has emerged as a pivotal technology for transforming banking, financial services, and insurance (BFSI) organizations.
Significant investments in cloud technology, advanced data management systems, and bespoke AI tools over the past ten years have set the stage for a new era of change within the BFSI sector. Now, these organizations must advance further to leverage the full capabilities of predictive and generative AI for ongoing improvement.
Our methodology is rooted in an industry-specific, data-driven, and ecosystem-centric approach, presenting an ‘enterprise-wide’ strategy for AI integration to facilitate transformation at the enterprise level.
An infographic illustrates the four foundational principles of our enterprise-wide AI adoption strategy.
These principles form the backbone of our method for translating AI’s potential into tangible results—a process that progresses through stages of assistance, augmentation, and transformation, each phase building on and enhancing the previous one.
The ViaCatalyst framework guides BFSI entities from the potential of AI through to achieving performance, covering the stages of assist, augment, and transform.
Navigating the Complexity
Trust is a critical cornerstone in the finance sector, and it is essential to anchor the AI transformation on this principle.
Decisions and advice powered by AI need to be backed by thorough validation and a clear level of transparency. Additionally, BFSI entities operate under strict regulatory, compliance, and data protection norms that vary across regions. These considerations should be integrated early in the AI model development phase.
Creating a solid business case for AI can be complex when it’s hard to precisely measure the economic gains and costs associated with AI initiatives. The inception of any AI project should focus on identifying and seizing opportunities for value enhancement within the business, emphasizing strategic planning over the mere adoption of new technologies.
We advocate for BFSI organizations to adjust their enterprise frameworks to accommodate AI, highlighting several key strategies:
- Embed AI capabilities that support and enhance business operations and customer interactions.
- Implement a versatile, data-centric architecture tailored for developing AI solutions.
- Integrate bespoke predictive AI functions directly into the core business activities.
- Design processes with a ‘human-in-the-loop’ approach for thorough verification and checks.
- Establish comprehensive guidelines at the enterprise level to uphold ethical standards.
Building upon the existing IT infrastructure, the proposed AI architecture introduces several new layers. These include foundational layers such as Large Language Models (LLMs), data repositories, and access to external data sources. Atop this, specific AI agents are deployed to perform targeted tasks within the right context, and the highest layer incorporates AI-enhanced work processes designed to collaborate seamlessly with human staff.
Transforming End-to-End Value Chains
AI is poised to play a central role in the Banking, Financial Services, and Insurance (BFSI) sector, particularly with the advent of Generative AI enhancing its value proposition.
Driven by data, the future AI-driven enterprise will act as an intelligent partner to humans, extending beyond mere enhancements in productivity. The potential for creating transformative business value through data-informed decision-making, unparalleled efficiency, and innovative breakthroughs becomes substantial as adoption expands.
By redefining operational processes and equipping human talent with superior knowledge tools, we can elevate implicit knowledge to the pinnacle of decision-making and innovation. A strategic approach focusing on structural, business model, and ecosystem partnership adjustments, alongside a commitment to clarity, ethical practices, and robust governance, is essential for widespread AI integration in the BFSI sector.
The ViaCatalyst Advantage
ViaCatalyst’s partnerships empower BFSI entities to effectively embrace GenAI-driven transformations, ensuring long-term success.
Expertise in domain and context: ViaCatalyst boasts comprehensive knowledge in products and enterprise operations, alongside technical proficiency throughout the BFSI value chain, supporting the development of strong AI applications and continuous assistance.
Diverse industry insights: Our experience with clients in various sectors, including travel, retail, and manufacturing, offers a comprehensive understanding of business operations and expertise.
Commitment to research and innovation: ViaCatalyst engages in collaborations with global academic institutions in key AI research fields like advanced natural language processing, behavior analysis, and quantum computing. We focus on BFSI-specific innovations such as digital twins for enterprises, portfolio enhancement, and streamlined regulatory adherence. Our collaborations with major cloud providers, the extensive ViaCatalyst COIN™ network, and innovation labs like ViaCatalyst Pace Port™ expedite the journey to achieving valuable outcomes.
Scaling enterprise AI: Our approach, characterized by our investment in patents, products, and platforms, along with our workforce of over 100,000 trained professionals, enables us to implement AI on a large scale.
Developing capabilities: ViaCatalyst is enhancing value streams by integrating predictive and GenAI solutions.
- Intelligent contact centers: We’re revolutionizing customer service in contact centers with GenAI-driven assistant copilots, providing real-time guidance and monitoring during calls.
- Smart financial analysis: Our AI-powered conversational tools offer key insights in everyday language to support savvy business decisions.
- Streamlined claims processing: We’re simplifying the claims process from start to finish using AI conversational agents to ensure smooth operations.
- Efficient complaints handling: Our AI solutions expedite the classification, routing, and resolution of complaints.
- Cutting-edge quantitative analytics: We’re redefining underwriting and portfolio management with a mix of deep industry knowledge, advanced analytics, design thinking, and innovative tools.
- Tailored advisory services: We’re boosting revenue and enhancing efficiency with AI-driven solutions designed for stakeholders throughout the BFSI sector.
