How a Big Data Strategy Can Fight Insurance Fraud
In highly regulated sectors, such as the insurance market, combating fraud is oxygen for the operation. There is always a multitude of compliance issues, regulations, risk management measures and monetary consequences to be addressed.
At the same time, insurers have also understood that they need a Big Data strategy for various purposes. Not all, however, already use tools to detect fraud.
That’s exactly what we want to help you reflect on with this article. Continue reading and understand how Big Data can help insurers avoid headaches and financial damage!
What is Big Data
For starters, let’s remember the concept of Big Data and introduce a very interesting practice, that of Real Time Big Data.
There is no strict rule about the amount of data that needs to be collected in a Big Data Strategy.
In practice, companies that collect the largest number of data are also those that promote the most advanced analyses for their business.
What normally defines Big Data is the need for new techniques and tools to be able to integrate and process this data.
To use Big Data, you need programs that span multiple physical and/or virtual machines working together to process all the information in a short period of time.
Within this, a definition of the very secure Big Data concept is provided by Gartner, the most renowned IT research company in the world:
“a large volume of information, high speed and/or high-variety of information assets that require innovative and cost-effective forms of information processing that enable better insight, decision making and process automation.”
Real Time Big Data
What the most innovative companies have used to handle exponential volumes of data today is Real Time Big Data. This concept refers to a form of Big Data analysis, concentrated on data produced, consumed and stored at all times in an active environment. Such as analyzing the massive amount of data generated within stock exchanges, banks and agencies around the world.
The data analysis is delivered to the administrator usually through an analytical software dashboard, so that the indicators can be viewed, monitored and analyzed in real time.
How Big Data Can Fight Insurance Fraud
In a world where transactions and documents are digitally recorded, evidence is available to assist investigators — including data scientists — in the battle against harmful fraudulent schemes. The hardest question is: how to easily and quickly find this evidence?
That’s where an integrated Architecture of Big Data and Research emerges as a very viable solution!
The following is an example of how this can be deplyed in four steps:
- Public data, such as provider information, codes for healthcare procedures, etc., is aggregated and processed through the Big Data framework, which performs large-scale denormalization to distribute data across multiple tables and Fields.
- The processed data is then loaded into a search engine (a specialized platform).
- Machine learning and predictive analytics work to identify fraud alert signs and proactively detect fraud schemes.
- A graphical research-based interface is provided to researchers for evidence analysis and documentation.
In short, the Big Data architecture allows the insurer’s fraud detection efforts to be scalable, faster and more accurate. Because the system processes and analyzes each available data record, it also gives researchers more certainty in their findings.
5 steps to start your Big Data strategy
Generally speaking, here are the initial steps that are usually a part of Big Data strategy.
A Big Data project requires sophisticated planning and orchestration. Why is that? Because it stops and introduces new hardware, software, features, and datasets.
It will involve sets of technical tools never experienced by the insurer and possibly by its IT team. And it will gather never-before-integrated datasets.
New policies, procedures, training, and project planning need to be carefully procured.
Big Data solutions include Data Warehouse data, raw transaction data, and unstructured log data.
A Big Data financial solution can have trading, market, position, news feeds, customer reference data, web logs, and system logs.
Repeatable processes must be established for the consumption of each data source. Techniques inherited from traditional Data Warehousing, such as altered data capture, micro-batch processing, and real-time data flow still apply.
The paradigm shift to Big Data introduces a new role for the insurer: data scientist. This role requires profound knowledge of advanced mathematics, systems engineering, data engineering and business expertise.
In practice, it is common to use a data science team, where statisticians, technologists and business affairs experts solve problems collectively and provide solutions.
In addition, every Big Data strategy should include continuous monitoring and maintenance of the technical solution. As data volume and analytical requirements increase, solution configuration should evolve and grow.
The distributed system will need to have the nodes added, the redistributed/balanced data, the adjusted replication, and the configuration of all these items continuously optimized for optimal performance.
Before a Big Data project is launched, a strategic readiness test must be conducted to assess the adoption of the new paradigm.
Business analysts will need to be trained again or repurposed. The goal of switching to a Big Data platform may include changing from reactive analysis (Did our project/our research work?) to a proactive one (what should be our next step in this project/ investigation?).
- Loop lock
Now armed with a complete Big Data ecosystem, including recommendations created by data scientists, you can close the loop —feed the results of the analysis into the mechanism, which creates the customer experience: website, marketing department, strength of sales, product development and customer service.
In addition, the Big Data machine can now consume recommendations provided as a result of its analysis correlated with new customer behavior patterns and quantify its effectiveness.
any new initiative should be inserted into a well-planned Big Data strategy.
A tool, language, or platform alone does not provide a solution.
Design Thinking + Big Data
Finally, it is important to know that there is no single recipe for Big Data strategies. Each business will have to design the best strategy for themselves, taking into account its segment of activity and also considering its own particularities.
That’s why a very important initial step is to hire specialized consultancy. This supplier, together with the insurer’s internal IT team, will survey the needs and real opportunities. From that, they can draw up the best strategy.
Design Thinking is a very interesting solution to dive deep into the business and think of a strategy that presents real, tangible results.
What do you think? Did we show you how a good Big Data strategy can help fight fraud in insurers? Delve deeper into this theme; come see our webinar Unveiling the mysteries of Data Science!