Discover the New Insurance Industry and Modern Technology

Discover the New Insurance Industry and Modern Technology

Read Time:7 Minute, 27 Second

The rapid nature of digital transformation and technology adoption had revolutionized industries, especially BFSI. Complex and financial data sets involved, make it imperative for the industry to seek better analytics to foresee the outcomes. That is why predictive analytics is important. Predictive business models have proved to enhance customer experience, revenue, risk management, and provide a competitive edge. Hence, in this article, we will discover the new Insurance Industry and Modern Technology.

Disha Indrakant Mishra is currently employed as the Analytics Engineer- Machine Learning at Aureus Analytics.  Aureus Analytics Pvt. Ltd provides Artificial Intelligence solutions to the insurance industry. The company offers customer intelligence to its wide client base and enables them to deliver the best customer experience through customer retention, loyalty, and lifetime value.

Disha has been driving several innovations through her portfolio. According to her, the key to these innovations is curiosity and a desperate need to solve a pressing business problem.

She is influenced by three major factors when providing innovative services to her clients

1. The existing infrastructure of the customer/client

2. Availability, volume, and quality of data

3. Capability and capacity to consume the solution offered

An example use case is a Cross-sell (XSell) predictive model suite, built on data. This is intended to improve the sales through existing customers of the company’s client. Disha reveals that apart from the problem statement of ‘whom to sell’, the company delivers recommendations on ‘what to sell’. Recently, it has deployed the final phase of this pipeline project with the problem statement of ‘how much to sell,’.  This suite will eventually provide a holistic picture to the clients, helping the client in targeting existing customers for more product penetration, increase in revenue, and customer stickiness.

Among other noteworthy innovations spearheaded by Disha is the use of government data available in the public domain to assist businesses, who were in the strategy and planning phase, with no data generated from within. Here solutions like market potential and business expansion models helped to identify striving geographical locations for the business. Algorithms like K-Means helped in finding similar viable locations for business operations. Data points like the census demography, population density, the smart city plan of the government, RBI data showing financial capability, and competition penetration helped in decision-making. This novel solution, also assisted in recruitment forecasting, by identifying the skilled labor & talent required to support the business.

An example of designing predictive “new-business” models, that used a bucket intervention technique as a deployment strategy, also comes to mind. It was based on the risk or chance of the event. The innovation within this suite was with the model evaluation (test and control) strategy. Since approximate average revenue of INR 15-20 lakhs per lead could not be ignored for a control group, leads were categorized as hot, warm, and cold basis predicted conversion probability across groups and shuffled. The approach focuses on the extent of intervention essential for the lead based on the capacity, volume, and availability of resources. It also gave attention to the strategy for utilizing various communication platforms preferred by the client for maximum returns.

Amongst other compelling use cases is the use of CLTV (Lifetime Value) with segmentation by Disha. This leveraged basic segmentation on the demographical, geographical, and purchase behavior of the client’s customer universe, thus helping in better business management and understanding. Customization and relevance of campaigns improved communication between the client and customers. Disha shared that this specific CLTV innovation in sync with the cohorts obtained had a positive impact, with better KPI management.

To increase engagement among life insurance advisors pro-actively, an advisor model suite is outlined to target and prioritize the advisors in terms of productivity and revenue KPIs. The suite prioritizes advisors basis a predicted probability, giving an added advantage compared to a scenario without the model. Extensive and in-depth feature engineering for the model overcame the challenge of lack of data dimensions. It helped in validating the business hypothesis, influence and improve the results. The client focuses on retaining the performance of top / high probability advisors while improving the performance of the low probability advisors, thereby optimizing their effort.

Additionally, the ingenious use of statistical concepts like inflection points diversified her portfolio. This initiative by Disha was a part of the descriptive analysis on agent selling behavior and the end trajectory of the agent lifecycle. This particular innovation proved beneficial for the insurance industry in sustaining agent performance. It was also instrumental in effective planning of the various reward & recognition programs that assist in motivating and elevating agent morale, thereby backing business and aiding in desirable longevity, even in times of a crisis like the Covid-19.

All the solutions and innovations mentioned above anchor the demands in the industry. Most of the solutions Disha developed were a result of the requirements that support the business problems faced while others resulted from observing struggles faced by her clientele. Disha acknowledges that most of the clients use deployment strategies provided to handle the problem better, operationally. These businesses are better equipped in dealing with the challenges faced since they have a clearer picture of prospects.

BFSI industry, in particular, is still in transition from a traditional system to a digital system on the focus. Analytic solutions and services developed by Disha aid in the change, along with providing intelligent solutions for the problems.

A Sea of Benefits Driven By the Innovations

ROI maximization, efficient KPI management, intelligent decision-making processes, benefits in operation management, etc. are the potential positive impacts of the innovations. Most predictive models developed by Disha give an edge in handling the volume of data points. Discover the New Insurance Industry

Disha cited an example of a product that was going to be discontinued and the client’s need was to focus on generating a maximum number of sales before closing. In this case, the predictive model developed by her, helped the client identify up-selling or cross-selling opportunities within the customer universe, collecting approximate revenue of Rs.100Cr through the model. It also helped the client achieve his goal before the deadline. This deployment strategy aids in better ROI management. Discover the New Insurance Industry

Disha reveals that the solutions developed by her have helped the firms in the conversion of a few POCs, retention, and upselling of services offered to the existing clientele along with better client management by building long-lasting relationships. She brings to notice that the automation done inside the company has helped in speeding up deliveries by decreasing the project timelines and better resource (employee) utilization per client.

Fighting the Challenges with Keen Observation

During the development of these innovations, Disha faced challenges that mainly revolved around the capability and extent of consumption of services provided to the business stakeholders. Since analytics is used to influence decision-making, she identified the prevalence of inhibitions to incorporate solutions amongst the business users. Another challenge she encountered is the IT infrastructure limitations of the BFSI client seeking the solution. Disha shared how the team used primitive and free open-source tools to enable integration and smooth camouflaging of the service with the existing client infra, which influenced the extent of innovation. Discover the New Insurance Industry

Disha promptly mentioned the potential drawbacks of her innovations. This includes instability and below-par performance of the predictive models due to delays in deployment and implementation. She affirms that sometimes interventions are not designed based on the suggestions prescribed, and hence leads to below expectation and unsatisfactory ROI management. Discover the New Insurance Industry

An Inquisitive Talent Delivering Intelligent Business Solutions

Disha describes herself as a self-driven human with an insatiable thirst for answers. Disha brings with her the collective expertise of over 5 years in the field of machine learning and AI to date. Her role mainly revolves around solving business problems using technology and analytics. She is largely involved in building predictive models, performing descriptive analysis, and prescribing deployment strategies as a consultant for the company clientele.

She is recently awarded as the Young Data Scientist of the Year – 2021 by India’s most prestigious Inflection awards, partnered by NASSCOM, for her innovations and excellence. She is also one of the recipients of the AI & Analytics Rising Star 2021. Discover the New Insurance Industry

Disha is a graduate from NMIMS Mumbai in Computer Engineering and is certified in the field of Information Storage and Management, Cloud as well as Big Data Analytics and Data Sciences by Dell EMC.

She believes in counting on experience that is gained out of quality work done, passionately. Disha has also previously been a College Ambassador to Dell EMC in her degree college because she understands that learning enables having worthwhile contributions to life, and hence as an ambassador it serves as an opportunity to make learning accessible to as many lives as possible.

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