The financial and banking sector has transformed in the past few years, improving efficiency, convenience, and security. Generative AI is now bringing a significant shift. Various surveys disclose that 78% of financial institutes have implemented Gen AI or are planning AI integration. More than 61% precede impact on the value chain, improving responsiveness and efficiency.
Globally, institutes expect a 5 to 10-year timeline for complete automation harnessing, investing in segments with immediate benefits, like customer support and cost reduction.
 Generative AI has made a huge impact on the entire value chain, making it more efficient and responsive to dynamic market |  Gen-AI-empowered virtual assistants improve user experience by minimizing wait time reducing redundant and improving interaction |  Gen AI has transformed the banking & financial sector by driving service innovation through research methodologies, hearing customers' voices, and providing valuable market insights to enhance and reshape features in this evolving industry |  Gen-AI in financial services acts as copilots and adapts to understanding unstructured data, serving crucial information for credit managers. It excels in knowledge management by organizing SOPs, policies, and onboarding kits |
Almost every business has witnessed significant change in the past few years due to a digital revolution, and the banking and financial sector is no exception. For a long time, AI has propelled the digital evolution of industries, but with the growth of Gen AI, businesses are witnessing a significant shift.
Along with technology expenditure, finance organizations begin to associate specialized teams and set funding for Gen AI deployments. Globally, many financial institutions predicted that harnessing the Gen AI potential may require considerable time, somewhere between 5 to 10 years. As a result, they are investing in areas that return readily achievable benefits.
A similar trend is witnessed in Indian financial institutions also. For many Indian financial institutions, generative AI delivers measurable business outcomes that improve customer service and cost reduction. Gen AI is continuously proven to deliver business outcomes that ultimately impact these areas.
Improving Customer Experience
Serving and improving customer experience is now on priority in the financial and banking industry. As it impacts customer satisfaction, trust, loyalty, and ultimately reason for the institution's success itself. At this stage, there is a growing need among Indian financial institutions to use Gen-AI-empowered virtual agents to handle customer queries.
Using Gen-AI in the existing process lets banks transcript customer calls into data, search knowledge repositories, and provide real-time responses to customers. This improves customer experience since it reduces the customer wait time, reduces repetitive questions, and improves interaction with a bank.
Cost-benefit analysis and strategic considerations
Identifying use cases makes it necessary to put substantial effort into prioritization, cost-benefit analysis, and strategic consideration for technology and data architecture. Therefore, worldwide banking institutes are exploring 7-10 use cases. One survey suggests, that 45% of participants address identifying use cases and insufficient focus on Gen AI initiatives as the primary obstacles while implementing generative AI.
Impact of Generative AI on the financial services industry
Adoption strategy of Generative AI
The adoption of generative AI includes strategic decision-making for banking institutes. Major implementation strategies include purchasing and fine-tuning pre-trained models and building large language models (LLM) from the start. One survey discloses that 31% of organizations are confident in creating LLM internally, rest plan to take external assistance, creating an alliance for implementation.
In the financial sector, Gen-AI-empowered virtual agents can improve user experience by minimizing customer wait time. Balancing between execution risks, costs, and viability is necessary for CXOs. AI models for an in-house specific use case offer tailor-made solutions, whereas pre-trained models provide efficiency for non-novel scenarios.
Global financial organizations established the AI Center of Excellence to manage unified standards for data training, model development, and architecture. However, there is a cybersecurity risk when using third-party LLM, and regulatory queries can arise with internal models. Collaborating with SMEs (Subject Matter Expert) can help design a centralized generative AI marketplace for repeatability across various business functions.
Three steps to the future
How we engage with the technologies has rapidly evolved. A few decades ago, we shifted from punch cards to green screens on IBM mainframes. Then we learned to use a keyboard and mouse in the PC era. And now, from the mobile web birth, the digital world is compressed into small phone screens.
Cognitive interfaces modify the app's outlook
How we engage with the technologies has rapidly evolved. A few decades ago, we shifted from punch cards to green screens on IBM mainframes. Then we learned to use a keyboard and mouse in the PC era. And now, from the mobile web birth, the digital world is compressed into small phone screens.
Generative AI has the potential that let us out of the ecosystem of the mobile phone and the app. With LLM models like ChatGPT, we are already searching the internet in new ways. We have started using AI chatbots to hunt the internet. Now we can upload images and PDFs to grope the inside knowledge in a descriptive form. We are now welcoming cognitive agents who act on our behalf with simple training and instructions.
Foundation models trained on text, images, voice, and videos, will change the digital interfaces that we use today.
AI agents modify work
AI-powered agents built and trained on the foundation of your knowledge base can transform how you work.
Imagine you run a finance company. You have created an embedding of your policy documents. There are multiple mathematical expressions of your business rules that might be in an unstructured form. These rules and flows are now scattered within your organization - resting as Word documents or PDFs on an employee's laptop. Moving forward, a knowledge base is a spotlight of your business workflow and gradually becomes the only source of your enterprise know-how.
On this foundation, you can imagine an autonomous agent that automates the customer's financial and medical document processing, simplifying search and understanding of various policies and helping improve recomputing paradigm al-time communication. The agents use Gen AI to summarize's customer's documents, and then create a well-structured dashboard to present all the possible case findings, document summaries, and recommendations on premium for the agents to read and make decisions.
A new general computing paradigm is developed
Foundation models are not apps by themselves. They are foundation components of the upcoming generation of application architecture. Generative AI is transforming the approach of app building. The front end of apps shifts from mobile apps to conversational interfaces. A major part of the business rule engine functionality will move to knowledge basis and vector space. Agent framework will arrange the processing logic that integrates intelligent front ends with the enterprise knowledge knowledge base. As we think about our data architecture and shift to more redefined data-oriented architectures, we will need to integrate this with foundation models.
This entire architecture will use fit-for-purpose cloud platforms that will increasingly specialize to industry and functions. Also, Gen AI paces up the software coding by converting natural language instructions to more complex code. This ultimately reduces the cost of digital apps and increases in speed of new building and innovation.
As technology, entrepreneurs develop and tug new foundation models and then make them useful for everyday work by building apps made on them. It will bring a change in the functioning of enterprises. It is also like to transform traditional business models while creating new ones. This will be an exciting, if deafening, journey as Gen AI becomes an integral part of business.
Does your business need Gen AI?
0%Yes, Generative AI is useful for my business
0%No, I don't need Generative AI
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