Co-authored by : Santhanam Rekha
Financial Services industry is continuously evolving where Innovation is the key to sustain in this cut-throat and fast-paced competitive sector. With the ever increasing demand on the quality of services from the end customers, it is imperative for the companies to formulate ingenious strategies to improve their customer satisfaction and experience and offer services at a quick turnaround time.
Data – the Game Changer
Gone are the days when the top echelons and Analysts of FS companies wade through a pile of files to get necessary information to take crucial Business decisions, which is a cumbersome task and is prone to errors. Financial Services Industry is a “Data-Rich” industry and “Data” being touted as the driver of growth of this sector, the reliance of Analytics in this field is inevitable. Many Industry experts claim that “Data” is the next “Oil” that is going to rule the roost for many decades owing to its necessity and perpetuity of demand.
Big Data – ‘Big Bang Revolution in the World of Analytics’
“Data” is always abstruse and in scattered form. To make it more palatable, it needs to be processed into a useful information for the perusal of the Managers.
Big data is a ‘buzz term’ that describes any humongous amount (ranging from TeraBytes to Petabytes), of raw structured, semi structured and unstructured data which can be mined for useful information from variegated data sources ranging from Satellite Data to Government reports.
Insurance Industry – Replete with Data
Insurance Industry is replete with Data and Big Data Analytics helps insurers determine and foresee the risks based on the Data garnered across departments and from a wide spectrum of sources viz., customer Interactions and Social media, Government Reports, data from sensor devices.
Risk Assessment and Personalized Risk Pricing
One of the Key areas where actuaries focus is “Risk Assessment and Pricing” as Profitability of Insurers relies heftily on the Risk Selection by their Underwriters. For long, Insurers charge premium to their customers based “Class Rating” - For eg: In the Automobile Insurance Segment, a Rash-driver and a Driver having good track record without history of accidents might be charged at the same level of premium, just because they both own a same brand vehicle of the same brand or of the same age. Though they possess similar characteristics, their driving patterns might differ, so the premium shouldn’t be the same.
To overcome these limitations, and to accurately price premium for their products, Insurers started looking at innovative ways of charging differential premium based on Customer’s driving Patterns. This can be achieved with the help of Telematics (Telecommunication devices fitted to Vehicles) to track and monitor their customers’ driving patterns to determine the risks involved in insuring them. As Big Data Analytics and IOT (Internet of Things) work in tandem, data sent from these devices can be processed using Big Data Analytical tools, so that Underwriters can take informed decisions as whether to load the premiums or not to renew the policies of Rash-driving customers.
In Health and Life Insurance sector too, the wearables like Apple Watch, Fitbit, etc., play a phenomenal role in predicting the likelihood of losses from their customers, based on the activities they perform.
Role of Big Data in Accurate Reserving
One of the key challenges insurers face is predicting and earmarking Reserves for Claims and future losses. At the outset of claims notification, it is impossible for the insurers to predict the duration of the claim. Accurate reserves can be pegged as the loss adjusters start and progress with the investigations.
Since accurate reserving reflects the efficiency of Claims Department, and Long-tail Liability claims take years to go for settlement, it is difficult for the insurers to earmark accurate reserves for such claims.
With Big Data Analytics in place, Claim adjusters can more accurately estimate loss reserves by comparing a loss with similar characteristics. Analysts can delve into the history of such claims and compare the current claim with older claim of similar loss. Whenever the Claim is updated, the loss reserve will get calculated automatically, so that Claims Managers can exactly know how much to be paid from the reserves to meet future claims.
Crackdown on Frauds
In Insurance Industry, Fraud plays a spoilsport in the Balance sheet of the Companies by way of Revenue Leakage. Insurers are losing out Billions of Dollars of their revenue every year owing to Fraud and the unscrupulous practices by service providers.
Analysts can detect frauds and put a crackdown on the same in a timely manner through effective Data Management and Predictive Modelling.
Analysts can match various parameters and Data elements of the Claim against the profiles of Past claims and if there is a relevant match, they can delve into these claims and bring them under their radar for further scrutiny.
For eg: In Auto Insurance segment, there might be a large concentration of claims from a same “Garage shop”, which suggests the suspicious association of the Garage shop with the Fraud committers.
Understanding Customer Insights and Demeanor
FS sector is Customer-centric and with the fierce competition in this sector, companies are finding it difficult to retain their existing customers and bring new customers to their spectrum.
From the information garnered from Call Center Data, Emails received from the customers, their comments and feedbacks on the products/services rendered, help insurers to have a 360 degree view of the customers’ behaviors, thereby create a unique profile for them
In Insurance Industry, staying competitive and being time-to-market is imperative. Big Data Analytics proves to be a game-changer due to the abundance of Data, which forms the backbone of this industry. Adoption of Big Data Analytics, however, is still in the nascent stage and its potential is yet to be realized to the core in Insurance segment, compared to Banking Segment. Companies adopting Big Data will up the ante, and have prime-move advantage when compared to their peers in this segment.