Predictive Analysis-Business Booster of BFSI space

By Mukul Jain, SVP & Head-IT, DHFL Pramerica Life Insurance

Financial services sector has always remained lucrative targets for cyber criminals, but brand reputation/ credibility becomes all the more crucial factors for growing companies like ours i.e. DPLI , where we not only face business challenges from our peers but also outside threats in the form of cyber attacks which may hamper our growth and impact our reputation.

We have recently observed a gradual shift of cyber security incidents towards financial motives. Cyber attacks are now being vectored on to diverse systems such as email communications, social media, PoS M/Cs mobile devices etc. in the forms of email spanning, phishing DDoS attacks etc. So companies need to take into consideration these scenarios regarding the mitigation plans in case of a cyber attack in the BFSI domain.
First and foremost, organizations need to do an analysis of their security posture and capabilities as funds are limited. Based on this analysis, a response framework considering the current gaps needs to be created to handle any unforeseen scenario like cyber-attack. An effective IT governance model is the key mitigation plan to counter cyber attack which should include policies and procedures, codes of conduct, training and incident response procedures.

Profile of cyber criminals can vary from script kiddies to professional hackers including cyber terrorists, phishers, spammers and also insiders in form of employees with malafide intent. So it is a 360 degree fight rather than one sided approach, to counter such varied foes, companies need to have protective shield in the form of not only policies and procedures such as security incident response teams but active involvement of all levels of management and enlightened employees., This is further complimented by technology based defenses such as advance firewalls, SIEM, IPS / IDS, DLP, antivirus etc. Having all the paraphernalia still may not serve the purpose as strong governance model remains an enigma for most of the organizations. So an effective Information Security governance model overarching all the defenses is the need of an hour.
Moreover in BFSI sector, the companies generally have a lot of information about the customer. Apart from demographic information including age and gender, one also has information about family, income, product chosen and its purpose etc. All this information merged with the social profile of the customer and then applying predictive analysis can be used to target a specific product to the customer basis the life stage, disposable income and customer needs. A home loan customer can be offered term insurance to cover the liability of the loan, a recently married individual can be offered family floater health insurance, parents can be offered products designed to support children studies etc.

Predictive analysis can help us understand the what, who, why and How of our data i.e. helps in understanding the behavioral data, descriptive data interaction data and attitudinal data through which we can mitigate risks associated with price sensitivity, churn models , customer life time value, sentiment models , acquisitions models etc.

But this has to evolve continuously to be able to assimilate the new information being made available about the customer through many modes of interaction with him/her and factor it in the predictive analysis model and presented.
Predictive analysis if used appropriately can turn out to be a very strong catalyst in boosting business in BFSI space. It is about increasing the share of wallet as well as mindshare by introducing/offering new propositions to the customers using the available data of the customer as well as well of various other customers who seem to have similar characteristics identified through predictive analysis.
Predictive analysis can be used in many ways in insurance sectors as below:

Customer analytics:
• Increase revenue through improved up-sell and cross-sell
• Increase profitability by offering right product mixes to right segments
• Accelerate offer acceptance
• Improve customer satisfaction
• Learn customer attitudes

Fraud Management:
• Reduce costs towards early claims by predicting cases at underwriting stage
• Improve overall CSATs by Segregating & fast tracking clean claims, while identifying fraudulent ones

Persistency Modelling:
• Establish factors that drive lapse behavior, their correlations and interactions
• Build a predictive model for persistency

Smarter advisor management enables insurers to:
• Improve the efficiency of agent recruiting by scoring applicants
• How similar is the profile of the applicant to existing successful field force?
• Prioritize which applicants to pursue
• Establish the training needs of an advisor basis performance
• Predict the propensity of an advisor to churn

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