By John Standish

Someone once told me that when you become the leader of organization, there is usually not a blueprint or map given to you to follow on a path to success. Indeed, it is healthy and good for personal and professional growth to step out of your circle of comfort and embark on new assignments and positions with more responsibility, but how can you ensure success? “Best practices” is a cliche that means a lot of different things to various professionals. Analytics can mean different things to business and government executive leaders. However, when you leverage best practices and analytics together in insurance fraud investigations, a powerful tool and business model is created that will have significant results in the reduction of fraud and provide a great return on the investment (ROI) in the anti-fraud programs.

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Corporate and government leaders are at a crossroads regarding risk management, social media, and the ever sensitive ROI questions asked by stakeholders and governing institutions. I believe that the four key components of best practices for insurance fraud programs are:

• Risk Management
• Training
• Policies and Procedures
• Performance Evaluations

Hiring the right people, at the right time, for the right job are basic steps in any business process. Insurance fraud is not a simple crime to investigate. Indeed, there are basic schemes which have not changed much over time; however, professional white-collar criminals learn from others mistakes made in their illegal activities, which ultimately led to apprehension and conviction. Managing your daily risk in conducting investigations can be daunting, yet if you are training your staff in current schemes and trends, policies and procedures, and documenting job performance through a meaningful performance evaluation process will deliver consistent and excellent results in your anti-fraud program. So, how do you keep your investigative units current and efficient in operations? There are four key components to advanced analytics:

• Data, lots and lots of data
• Statistical and quantitive analysis
• Explanatory and predictive models
• Fact-based management to drive decisions and actions

People are communicating more and more through social media platforms like Facebook, Twitter, Foursquare, Google Plus, Pinterest, etc. The number of social media websites are growing everyday in our flat, global economy world. Risk is always the number one concern of the executive leader when it comes to operations and personnel. How do you know that you are meeting your goals and objectives? Are you agile enough to change direction under a critical incident; in other words, can you predict (and prevent) an incident that will drain your resources? Understanding the core elements of advanced analytics will help you with all of these critical leadership issues which confront us everyday.

Join us on June 26th for a Special Presentation by John Standish >

Just utter the word “analytics” in an executive staff meeting, and someone will usually summon the information technology manager. A lot of people think of spreadsheets, data base searches, dash board style alerts and warnings about budget pressure points, or a due date for a big project. Analytics is a lot more than that. I really like the definition: “Analytics is a range of capabilities across the intelligence continuum.” Law enforcement agencies have different levels of intelligence, some categorized as “actionable” or used everyday in the prevention of crime and disorder in our communities, all the way up to homeland security threats. In the corporate world, analytics touches our lives everyday in many ways that people are not aware of. For instance, the product you purchase in the grocery store, the fastest route that your package will be shipped and travel to you, and consumer sentiment about a product discussed in blogs and social media websites is all facilitated by advanced analytics. Analytics is not a single software solution, nor is it a single management or decision making tool. Analytics is all about leveraging data with best practices that results in a more informed decisions.

Top 5 Reasons Why Insurers Do Not Use Analytics

James Ruotolo, Principal for Insurance Fraud Solutions at SAS (www.sas.com) recently wrote an article on why insurers do not use analytics to connect the dots. James, with poise and confidence, set the record straight on why insurance carriers (and I strongly urge government agencies, too) how they can they can use analytics and yield a quick return on the investment:

1. “We don’t have enough data!” Fact: Not true. Insurance carriers and government agencies collect enormous amounts of data in their business process. A number of statistical approaches can be created to build a solid predictive solution. For instance, when you combine business rules, anomaly detection (outliers), and social media analysis together, you can identify suspicious claims even if there is no prior claim history.
2.  “We don’t have good data!” Fact: Data quality issues do not stop a successful implementation of technology solutions. The cliche of “garbage in – garbage out” is alive in well in our business world, we all know this. However, recent advances in data cleansing methodologies allows all kinds of silo data sources to be combined in an advanced analytics solution.
3. “Our data is to fragmented!” Fact: You do not have to gut your existing technology platform to create a solid fraud detection solution. There are integration tools to bring together key elements of your various internal systems. Remember, the more data you have, the powerful the analytics will be.
4. “It’s all in the notes!” Fact: Text analytics can rapidly comb through unstructured text revealing facts and connections a lot faster, which means that the suspected fraud will be discovered a lot sooner.
5. “We can’t handle any more cases!” Fact: It is not about processing more cases. It is all about working the right cases to make an impact in reducing fraud. I strongly advocate that if you do not identify the true cost drivers in fraud (the licensed professionals, administrators, cappers, stagers, and other individuals controlling the criminal enterprises), we will never truly reduce the amount of insurance fraud in our communities.

The key to analytics are the three “R’s:” The right data, at the right time, to the right people. This complements the right people, at the right time, for the right job. All of these are the key ingredients to best practices in fraud investigations. We have to get better in preventing and identifying insurance fraud, and waste and abuse in government programs. Having the right staff, coupled with best practices and a robust advanced analytics system on the front end of your business process will only ensure success.

Join us on June 26th for a Special Presentation by John Standish >