Businesses involved in banking, financial services and insurance sectors typically have loads of information related to their customers and prospects. The info can be associated to claims, opening accounts, transactions, requests, etc. Also the data can have the potential to create marketing opportunities to those who are looking to make some profits.
However, as a matter of fact not all data is reusable, as much of it is of low quality or unreliable. That’s because customers and prospects enter junk data in various fields to cut short the process.
Further, data is captured in multiple formats and from multiple sources. There are claim applications that originate from legacy systems; IT systems used by other departments, homegrown SIU (special investigative unit) case management systems, third-party vendor systems that feed medical bills, etc.
In addition to the incompatibility between these disparate systems, useful or insightful information is captured in long strings of data, which makes it difficult to extricate the relevant text.
As the world of technology has progressed, research in this area has helped evolve some techniques for detecting fraudulent data. Want to know how….? Check the lines below
- Better integration between data resources- In order to ease the process of fraud analytics, data from multiple resources must be integrated into one centralized system which makes it easy to extract and compare data. This approach will mainly help insurance companies which handle claims, policy info, bill & invoices, medical reports and clinical info from numerous data points. The first step then is to create an interface that will integrate all the data and make them compatible with each other. And that is when fraud analysts can detect erroneous and redundant info.
- Create mechanisms to gather missing or erroneous data- Sometimes customers or prospects generally tend to fudge the data. This creates data islands which are often futile and irrelevant. This is where data quality tools that are adept at identifying, repairing, and replacing missing or erroneous info proves helpful. They may be available in another system, or can be extracted from the existing data. Your Fraud Analytics team must create such mechanisms, along with standardizing data formats across multiple sources of information.
- Unify entity info- Once all the data is integrated and missing or erroneous information rectified, the next step is compile all entity information in one place. Entity refers to the individual or company who could be present across different claims, applications and other documentation. Once the entity is identified as the same individual or company, all information about the person has to be aggregated into one single place. This makes it easy to detect fraudulent information, or any suspicious activity on part of the entity.
- Better ways of handling unstructured text- Much of the data captured by insurance companies is in text format. However, there is no consistency in them as there are innumerable abbreviations, acronyms and jargon used by multiple users, not to mention typos and factual errors. The organization must harness techniques such as machine learning, natural-language processing, thesaurus of industry keywords, etc, which will make Fraud Analytics easier, and more effective.
Managing data better is critical to detect fraud. Organizations must invest in tools, software, systems and skilled human capital to tackle the problem.
Contact DNF today to begin the conversation about upping your Data Security, for your firm and your customers.