Behavior Analytics remains a neglected area by financial services firms

Manav Sachdeva, Principal Analyst, Gartner shares his views on how companies can leverage data analytics in business transformation and creating efficient customer connect.

Edited excerpts:

Q: Analytics has been one of the key focus areas for BFSI companies. What is the extent of usage of data analytics in the sector in India?

Sachdeva: Analytics goes in tandem with the company’s data strategy. However, many Financial Services (FS) organizations, globally and in India, are revamping their data and analytics (D&A) strategies by improving quality of their data assets, attaining data mastery and identifying solutions with ease of integration to their business systems.

Currently, analytical maturity of FS industry is ‘mediocre’ at best, with most companies utilizing analytics for:

  • Back-office optimization: such as for financial analysis, risk and regulatory management and reporting.
  • Customer analytics: owing to readily available marketing solutions offering 360-degree customer view and assisted-selling options

Behavior analytics remains a neglected area by FS organizations but allows hyper-personalization of products/services and customer experience. Banks and insurers, globally, are increasingly experimenting with this as many analytics and AI Fintech vendors have emerged in this segment. This, along with API-driven architecture, will enable them to work with third-party partners to quickly and inexpensively introduce innovative products and services.

Q: Data Analytics is mostly addressed in conjunction with AI and ML in the BFSI Sector. What are the potentials?

Sachdeva: As industry boundary blurs and FS businesses begin to work with partners from within and outside the industry, an analytical engine at the heart of this model will enable true value sharing and scalability. Any technology on standalone basis cannot deliver transformation, but it’s a combination of technologies that are applied to accomplish a digital business moment. For instance, a mix of AI, IoT and automation (RPA) can be used to transform an insurance claims process.

Considering a particular use case – Advanced analytics, including chatbots and virtual personal assistants, artificial intelligence and intelligent automation are increasingly becoming integral to how FS firms support more effective customer service models, and potentially reduce operational costs. Applying these capabilities in conjunction with big data and more advanced understanding of business context is arguably considered the top priority for many financial services companies. Advanced analytics capabilities are also intended to assist the business in mitigating the complexity of interactions as well as optimizing process efficiency and information discovery. Access to and use of advanced analytics at an enterprise level will be key to achieve the desired results.

Q: What are the new applications of data analytics that companies are looking at today, other than the tried and tested areas?

Sachdeva: Until a few years ago, the financial industry thought that customer-facing processes would be the biggest beneficiary of the kind of transformative positive returns that analytical technologies like AI, IoT and automation promise to bring about. They were overlooking the opportunities the same technologies offer in making their organizations highly efficient and their processes lean and linear. Use of artificial intelligence, neuroscience and biometrics represents the next frontier of financial products and services innovation by understanding and delivering exactly what customers want.

Q: Could you throw more light on some specific use cases in the industry?

Sachdeva: Behavioral analytics for instance has been providing insight into the actions of user or asset.

Axis Bank (India) implemented an innovative speech analytics tool in its contact center to improve operations. The tool transforms recorded customer interactions from idle data into actionable intelligence, helping identify customer patterns and sentimental behavior. The tool can also identify scope for new opportunities and support instant escalation management.

Deniz Bank (Turkey) launched a neuroscience project to optimize its marketing activities. Using technologies such as electroencephalography and eye-tracking, the bank wanted to understand the real needs of the customer and provide matching services. After three months of implementing the findings of the neuroscience project, sales increased by 23 percent. Deniz Bank is now looking to apply neuroscience methods to all product scripts and branches.

Claims transformation is another area. This is impacting the core business process of insurance claim to dramatically decrease the time and cost involved.

Ping An Insurance (China) introduced its “Smart Fast Claim” artificial intelligence platform for loss assessment and risk management in the Chinese car insurance market. Using image recognition and deep learning technology, “Smart Fast Claim” can identify all types of cars and their outer parts; offer precise repair pricing for customers in real-time; and detect fraud during or after a claim.

Digital advisor model provides automated financial planning advisory or decision support for users and/or employees. Examples include:

  • GF Securities (China) launched a robot advisor to make investing easier by automatically offering intelligent stock and asset management advice for millions of investors, based on comprehensive and real-time information, well-tested finance model with risk control and powerful artificial intelligence.
  • Nationwide Advisory (US) pioneered a highly personalized, responsive and adaptable technology engine called the Analytics-Driven Advisor Experience. Employing real-time data management, analytics and artificial intelligence, this intelligent technology stack provides immediate personalization hyper-targeted to each advisor, delivering precisely the appropriate educational content and products designed to help them make more informed decisions, grow their business and aid in their clients’ financial goals.

Virtual assistant or chatbot, acting as virtual advisor or agent. This feature enables self-service model for customers who can get their basic financial transactions performed through this channel, without a human intervention.

Commonwealth Bank (of Australia) launched a virtual assistant for banking, powered by conversational artificial intelligence to assist customers with more than 200 banking tasks such as activating their card, checking account balance, making payments or getting card-less cash.

Q: Could you share some information about Gartner studies in the BFSI space?

Sachdeva: The Gartner 2019 CIO survey reveals that ‘Business Intelligence and Analytics solutions tops the list of technology investment for BFSI, with 46 percent of companies increasing their spending on analytics. However, the involvement of FS CIOs in the use of advanced analytics by FS firms is reported as quite low, with only 43 percent very or extremely involved, especially compared to top performers across industries (64 percent ).

According to Gartner’s 2019 CIO survey, Twenty-five percent of FSIs don’t use AI at all. Sixty percent of FS CIOs say their firms lack the ability to understand AI technologies, strategies and markets.


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