Providing Insight into Growth Possibilities and Potential Risks

I love big data inasmuch to date I’m still trying to comprehend all the potential it has.

Big data is extremely large data sets that may be analysed computationally to reveal patterns, trends, and associations, especially relating to human behaviour and interactions. -Google

This isn’t so much a post about big data. But big data is essential for the concepts herein. Predictive analytics and its potential to give businesses foresight being the key thing.

Predictive analytics is the branch of the advanced analytics which is used to make predictions about unknown future events. Predictive analytics uses many techniques from data mining, statistics, modeling, machine learning, and artificial intelligence to analyze current data to make predictions about future. -Google

I zeroed in on the insurance industry with this research. Based on a bit of what’s being done and the fact that this is an industry that could use some shaking up.

It’s fair to say that the business of insurance has a bit of a reputation. Be it related to poor service, an old fashioned approach, lack of flexibility or unsuitable covers. It comes as no surprise arranging for protection against future risks drops down to the bottom of the ‘to-do’ list for many individuals and businesses.

Most of us are unaware of all the potential insurance policies we could hold that would be of benefit to us. Other than the conscripted forms of insurance -auto insurance if you own a car and health insurance. We do not care about the other forms of insurance.

Insurance policy is a document detailing the terms and conditions of a contract of insurance.-Google

Policyholder is a person or group in whose name an insurance policy is held.

I went with the lead of there’s a need for a tool to help insurance companies predict policies their policyholders need and have interest in other than the traditional life, health, auto and education policies.

Small insurance companies have no definitive way of understanding their customers’ needs. So no way of offering them unique tailored policies that serve a large pool of people. Yet many insurance companies have a wealth of historical data about their customers. Why not work with underwriters and this data to develop products that fit the needs of businesses and individuals today?

The tool should be a way of making companies first to know of emerging trends among its policyholders, risk factors they face and possible outcomes. Besides optimizing on policy fit for policyholders,

Other highlights from my research so far include a report highlighting the global predictive analytics market to grow from USD 2.74 Billion in 2015 to USD 9.20 Billion by 2020. At a Compound Annual Growth Rate (CAGR) of 27.4% during the forecast period. The report further states the predictive analytics market is growing rapidly because of the transformation from traditional Business Intelligence (BI) techniques to advanced analytics techniques. Besides there is a massive surge of structured and unstructured data.(aka big data)

At the core of it, analytics is about solving business problems. Hence the essence of empowering the business with questions. So they come up with their own answers and formulate new questions to explore. This way, insights are more timely and valuable.

A common knock against analytics is that it focuses on past activity and delivers insights in reports that do not help companies craft effective competitive strategies. Predictive analytics aims to change that. By using statistical algorithms and machine-learning techniques to predict the likelihood of future outcomes.

Predictive analytics is also going more mainstream. Thanks to APIs (application programming interfaces), web services, predictive model markup language (PMML) and other development tools and technologies that make it easier for companies to include it in their business analytics programs.

Currently, launching a predictive analytics initiative can be quite costly. Good thing is, companies can use open source predictive analytics tools to keep costs low while exploring the possibilities of predictive analytics.

The above idea is just a drop in the ocean. The scope of technological solution using predictive analytics spans into many other sectors. If you are interested in continuing this conversation or have material/resources that could be of benefit, please find or share with me on LinkedIn.

P.S

Some other key challenges facing the insurance industry:

• Rising costs of managing claims and risk.
• Struggle to increase profitability.
• Most property, casualty, health and benefits insurers have limited underwriting and investigative resources to analyze, review, assess and pay claims.
• Competitive pressures to build new business, keep current customers and meet customer satisfaction requirements.
• Policy prices always seem arbitrary and too high.
• It’s nearly impossible to compare policies from different providers.
• In times when you get what you are seeking, there’s little transparency.

At the end of the day, we need insurance. We need it to evolve to a better tailored fit for consumers. So there can be less people hating insurance and more people benefiting from it. And still sustainable businesses out of it. Don’t get left out in this race to get the power and means to make smart real time decisions.

We are looking at a future where businesses move from insights to actionable foresight.