Someone once told me that when you become the leader of an 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 to embark on new assignments and positions with more responsibility. How, though, can you ensure success?
"Best practices" is a cliché that means a lot of different things to various professionals. Analytics can mean different things to business and government executive leaders. When you leverage best practices and analytics together in insurance fraud investigations, however, a powerful tool and business model is created that will create significant results to reduce fraud and provide a great Return On Investment (ROI) in anti-fraud programs.
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. Four key components to enhance the ROI for insurance fraud programs are:
- Risk Management
- 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 that have not changed much over time. Inevitably, though, professional white-collar criminals learn from the mistakes of others in their illegal activities (which led to their apprehension and conviction) to evolve their practice. Managing your daily risk to conduct investigations can be daunting, yet if you are training your staff in emerging schemes and trends, policies and procedures, and then documenting job performance through a meaningful performance evaluation process, they will deliver consistent and excellent results in your anti-fraud program.
So, how do you keep your investigative units current and efficient in operations? A working understanding of the power of analytics is the foundation for that expertise.
There are four key components to advanced analytics:
- Data — lots and lots of data
- Statistical and quantitative 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 and Pinterest. The number of social media websites is growing everyday in our flat, global economy. Risk is always the number one concern of the executive leader when it comes to operations and personnel. How do you know you are meeting your goals and objectives? Are you agile enough to change direction during a critical incident? In other words, can you predict (and prevent) an incident that will drain your resources?
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, database searches, dashboard style alerts and warnings about budget pressure points, or a due date for a big project.
Analytics is a lot more than that.
When Chiefs of Police and Sheriffs ask me to explain what analytics is, I tell them: "Analytics is a range of capabilities across the intelligence continuum." Law enforcement agencies exemplify this continuum, with differing levels of intelligence from the immediately actionable to prevent crime and disorder in our communities, all the way up to homeland security threats.
In the corporate world, analytics touches our lives everyday. 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 social media 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 more informed decisions. Unfortunately, this power is all too often left unused or underutilized in our industry.
Top 5 Reasons Why Insurers Do Not Use Analytics
James Ruotolo, Principal for Insurance Fraud Solutions at SAS, recently wrote an article on why insurers do not use analytics to connect the dots. James, with poise and confidence, dispels the myths and sets the record straight on how and why insurance carriers (and I strongly urge government agencies, too) can use analytics to yield a significant ROI:
- Myth: "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.
- Myth: "We don't have good data!" Fact: Data quality issues do not stop a successful implementation of technology solutions. The cliché of "garbage in — garbage out" is alive and well in our business world; we all know this. Recent advances in data cleansing methodologies, though, allow all kinds of silo data sources to be combined in an advanced analytics solution.
- Myth: "Our data is too 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 more powerful the analytics will be.
- Myth: "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.
- Myth: "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 to reduce 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), you 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 at 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 your success.