Products & Platforms
TCS BFSI Platforms
TCS BFSI Platform: Transforming Financial Services for Better CX
Highlights
- Consumerism of AI has raised customer expectations in the insurance industry too — pushing insurance technology to step up and deliver more advanced, customer-centric solutions.
- In the insurance sector, the focus of many organizations has been on exploring discrete use cases in a rapid manner, where pilot phases do not often interact with the enterprise systems. Thus, AI efforts remain siloed and unsustainable for scalability.
- A platform-centric approach is an essential requirement to bring together several technology utilities that deliver well outputs.
On this page
Consumerism of AI
Recently, my high school son prepared for his year-end exam. He wanted to revise by answering multiple choice questions. While I struggled to create various combinations of questions and answers to help him, he used a Generative Artificial Intelligence (GenAI) tool to generate more than 50 questions quickly. Like this, today, one can quote many instances from daily life where AI has made its way into.
AI is, perhaps, the next big wave of consumer technology after smartphones. The growth is several times faster than smartphones largely due to significant penetration and wider and affordable mobile networks.
Insurance industry opportunities
The insurance industry forever has relied on document-centric processes and dealt with decades-old products and processes with complex terms and conditions.
Searching through these details while engaging with customers creates a knowledge burden. GenAI, as a transformative force, can reduce the knowledge burden by equipping all agents with decades-worth knowledge during calls and services through other channels.
The six prime areas where there are potential applications of GenAI are:
- Product development: Sophisticated product modeling and pricing, AI-based product and process configurations, Natural Language Processing (NLP)-based rules configuration that allow insurers to launch products faster.
- Sales, distribution, and marketing: This could range from identifying leads, including cross-sell/ up-sell opportunities to lead scoring, prioritization in alignment with customer needs to developing personalized recommendations, and supporting paraplanning activities for advisers. Tomorrow’s virtual agents have the potential to provide digital advice with human empathy.
- Policy servicing: Policy and customer service can be dramatically improved in the contact centers, the back office, and transaction processing using agentic AI, Know Your Customer (KYC) details, customer communication, and complaint handling.
- Marketing and customer experience: Can be utilized in market research, personalization of experience, individualized targeting, personalized customer communication (including chat).
- Adviser and underwriting assistant (analysis and monitoring): AI can process, co-relate, and summarize large amounts of structured and unstructured data into structured inputs to advisers and underwriters in making decisions.
- Claims: AI can automate various aspects of claims processing, including data extraction, classification, and enrichment. This can also help detect and prevent fraud by using predictive models.
The obstacles
Despite the promise, many AI initiatives get stalled after the pilot phase. As per a recent viewpoint by MIT, 95% of Agentic AI pilots fail.
The reasons are multifaceted:
- Fragmented efforts: The focus of many organizations has been on exploring discrete use cases in a rapid manner, where pilot phases do not often interact with the enterprise systems.
- Weak business cases: Building a compelling business case for scale has been harder due to inadequate attention to customer/user context, engagement style, and end-to-end customer and process journeys, and integration cost.
- Lack of foundational infrastructure: Scaling requires robust data architecture, integrated platforms, and secure environment and guardrails similar to quality processes to ensure AI outcomes are consistent and as per regulatory expectations.
- Cultural resistance: Organizational inertia to adopt a new way of working, trying to replicate the current process into the future state model and lack of trust in AI systems can hinder adoption.
Strategy and outcomes
Having an AI-enabled process or addressing a customer demand might mean several AI techniques to be applied and hence, a platform-centric approach is necessary.
The platform must bring together several technology utilities such as Generative AI, Agentic AI, Document AI, Emotion AI and Predictive AI that deliver well outputs, which can, then, be combined or orchestrated to achieve the desired business outcomes.
We are collaborating with insurers to implement Document AI for faster and more accurate claims settlement, covering over 50 types of documents. It involves intelligent extraction, classification, enrichment, and mapping using advanced AI, NLP, and algorithms. Additionally, it automates insurance processes to guide users in providing correct information, supports various document types and formats, and enables intelligent document processing.
Review of end-to-end customer journeys: While using AI for discrete use cases, it is also important to review end-to-end customer journeys, expectations of customers, and colleagues' involvement. For instance, AI for Complaint processing should consider using varieties of business attributes from complaints data, journey profile, customer characteristics, customer engagement, and operations performance.
In-built safeguarding: It is important to tackle vulnerabilities and exploitation.
Making organization ready: In an enterprise, deployment of a process requires involvement of many stakeholders including Infrastructure, Security, Change Management, Risk, Compliance and Service transition.
Continuous improvement culture: The results from AI initiatives are sometimes achieved over a period through several rounds of improvements and adaptation. Hence, organizations need to build a culture of trust, experimentation, and continuous improvement.
The promise of intelligent, AI-based business models for insurance is exciting. As the industry stands at this new frontier, those who embrace AI holistically will lead the next era of insurance innovation.