Insurance Data Integrated Platform

The insurance industry today is poised for a paradigm shift in the way that technology is deployed to provide products and services to customers. This has primarily been driven by changing business needs and the innovations brought about by myriad insuretech firms, leading to an inevitable shift towards adopting the new digital innovations.

Analysts have forecast significant investments geared towards the digitalization of the industry and expect such investments to continue pouring in for several years. It is also expected that an increasing number of new insurance companies will be driven by technology companies to bring better products, services, and customer service in the insurance industry.

A forward-looking plan of action, sufficient operational flexibility, an effective implementation strategy, and a willingness to adopt digital disruptions in every aspect of their organization – those insurers that have all of the above can position themselves to leverage the impending digital disruptions to propel their organization to the very forefront of the industry.

DEALING WITH THE DIGITALIZATION OF THE INSURANCE INDUSTRY

These adopters of digital technology will have a clear upper hand against their competition. Suitably equipped to cut costs and design more attractive offerings, the digital insurance carriers are sure to acquire a whole new set of customers, thus increasing market share. Those who fail to quickly adopt the new technologies, on the other hand, will struggle to maintain their competitive positions in the midst of a customer-centric, price-sensitive market.

Data has always been at the center of the insurance industry, and despite the changes that are to come, data will continue to be the focal point of the industry. In fact, it’s set to play a bigger role to play than ever before.

The continued criticality of data in the insurance landscape is ensured by carriers’ need for information-driven strategies in the digitalized business scenario. They’ll have to leverage data as an asset, enabling automated decision-making in critical business processes, in order to thrive. This, in turn, is why a digital business technology platform – one that incorporates information management and analytical capabilities – will become a necessity in the future.

Without a system in place to support the analytics and reporting needs of the business, decision-makers may be left with no choice but to rely on conventional time-consuming manual processes those are more qualitative rather than quantitative in nature. This is bound to cause serious repercussions for the organization, ultimately resulting in missed opportunities and loss of competitiveness.

According to a Gartner study, the two following technology platforms are essential for any digital business:

  1. Data and analytics platform – This platform should consist of data management programs and analytics applications to enable data-driven decision making
  2. Ecosystems platform – This platform’s role should be to support the creation of and connection to external ecosystems, marketplaces, and communities

MFX

 

Click here to access MFX’s detailed White Paper

 

The General Data Protection Regulation (GDPR) Primer – What The Insurance Industry Needs To Know, And How To Overcome Cyber Risk Liability As A Result.

SCOPE

The regulation applies if the

  • data controller (organization that collects data from EU residents)
  • or processor (organization that processes data on behalf of data controller e.g. cloud service providers)
  • or the data subject (person)

is based in the EU. Furthermore, the Regulation also applies to organizations based outside the European Union if they collect or process personal data of EU residents. Per the European Commission, “personal data is any information relating to an individual, whether it relates to his or her private, professional or public life. It can be anything from

  • a name,
  • a home address,
  • a photo,
  • an email address,
  • bank details,
  • posts on social networking websites,
  • medical information,
  • or a computer’s IP address.”

The regulation does not apply to the processing of personal data for national security activities or law enforcement; however, the data protection reform package includes a separate Data Protection Directive for the police and criminal justice sector that provides robust rules on personal data exchanges at national, European and international level.

SINGLE SET OF RULES AND ONE-STOP SHOP

A single set of rules will apply to all EU member states. Each member state will establish an independent Supervisory Authority (SA) to hear and investigate complaints, sanction administrative breaches, etc. SA’s in each member state will cooperate with other SA’s, providing mutual assistance and organizing joint operations. Where a business has multiple establishments in the EU, it will have a single SA as its “lead authority”, based on the location of its “main establishment” (i.e., the place where the main processing activities take place). The lead authority will act as a “one-stop shop” to supervise all the processing activities of that business throughout the EU. A European Data Protection Board (EDPB) will coordinate the SAs.

There are exceptions for data processed in an employment context and data processed security, that still might be subject to individual country regulations.

RESPONSIBILITY AND ACCOUNTABILITY

The notice requirements remain and are expanded. They must include the retention time for personal data and contact information for data controller and data protection officer must be provided.

Automated individual decision-making, including profiling (Article 22) is made disputable. Citizens now have the right to question and fight decisions that affect them that have been made on a purely computer generated basis.

To be able to demonstrate compliance with the GDPR, the data controller should implement measures which meet the principles of data protection by design and data protection by default. Privacy by Design and by Default require that data protection measures are designed into the development of business processes for products and services. Such measures include pseudonymizing personal data, by the controller, as soon as possible.

It is the responsibility and liability of the data controller to implement effective measures and can demonstrate the compliance of processing activities even if the processing is carried out by a data processor on behalf of the controller.

Data Protection Impact Assessments must be conducted when specific risks occur to the rights and freedoms of data subjects. Risk assessment and mitigation is required and prior approval of the Data Protection Authorities (DPA) is required for high risks. Data Protection Officers (DPO) are to ensure compliance within organizations.

DPO must be appointed:

  • for all public authorities, except for courts acting in their judicial capacity
  • if the core activities of the controller or the processor consist of
  • by their nature, their scope and/or their purposes, require regular and systematic
    monitoring of data subjects on a large scale
  • processing on a large scale of special categories of data pursuant to Article 9 and
    personal data relating to criminal convictions and offences referred to in Article 10
    processing operations which, for the purposes of national

GDPR in a Box

 

Click here to access Clarium’s detailed paper

Creating a Data-Driven Enterprise with DataOps

Let’s discuss why data is important, and what a data-driven organization is. First and foremost, a data-driven organization is one that understands the importance of data. It possesses a culture of using data to make all business decisions. Note the word all. In a datadriven organization, no one comes to a meeting armed only with hunches or intuition. The person with the superior title or largest salary doesn’t win the discussion. Facts do. Numbers. Quantitative analyses. Stuff backed up by data.

Why become a data-driven company? Because it pays off. The MIT Center for Digital Business asked 330 companies about their data analytics and business decision-making processes. It found that the more companies characterized themselves as data-driven, the betterthey performed on objective measures of financial and operational success. Specifically, companies in the top third of their industries when it came to making data-driven decisions were, on average, five percent more productive and six percent more profitable than their competitors. This performance difference remained even after accounting for labor, capital, purchased services, and traditional IT investments. It was also statistically significant and reflected in increased stock market prices that could be objectively measured.

Another survey, by The Economist Intelligence Unit, showed a clear connection between how a company uses data, and its financial success. Only 11 percent of companies said that their organization makes “substantially” better use of data than their peers. Yet more than a third of this group fell into the category of “top performing companies.” The reverse also indicates the relationship between data and financial success. Of the 17 percent of companies that said they “lagged” their peers in taking advantage of data, not one was a top-performing business.

But how do you become a data-driven company? According to a Harvard Business Review article written by McKinsey executives, being a data-driven company requires simultaneously undertaking three interdependent initiatives:

Identify, combine, and manage multiple sources of data

You might already have all the data you need. Or you might need to be creative to find other sources for it. Either way, you need to eliminate silos of data while constantly seeking out new sources to inform your decision-making. And it’s critical to remember that when mining data for insights, demanding data from different and independent sources leads to much better decisions. Today, both the sources and the amount of data you can collect has increased by orders of magnitude. It’s a connected world, given all the transactions, interactions, and, increasingly, sensors that are generating data. And the fact is, if you combine multiple independent sources, you get better insight. The companies that do this are in much better shape, financially and operationally.

Build advanced analytics models for predicting and optimizing outcomes

The most effective approach is to identify a business opportunity and determine how the model can achieve it. In other words, you don’t start with the data—at least at first—but with a problem.

Transform the organization and culture of the company so that data actually produces better business decisions

Many big data initiatives fail because they aren’t in sync with a company’s day-to-day processes and decision-making habits. Data professionals must understand what decisions their business users make, and give users the tools they need to make those decisions.

DD Enterprise

Click here to access the ebook Data Driven Organizations