Since the start of the COVID-19 pandemic, SAS has formed dedicated global teams to predict and monitor the pandemic’s course and identify the likely impact for customers. In Turkey, we have identified three main risk areas for insurers and have set out some strategies to help our clients respond, recover
Most insurance companies depend on human expertise and business rules-based software to protect themselves from fraud. However, people move on. And the drive for digital transformation and process automation means data and scenarios change faster than you can update the rules. Machine learning has the potential to allow insurers to
This is the seventh and last part of the blog post series “A practical guide to tackle auto insurance fraud”. In the first six articles of the series we drilled down to: Data Management and Data Quality as the basis for fraud detection analytics. Business Rules and Watch lists techniques
This is the sixth of the seven parts of blog post series “A practical guide to tackle auto insurance fraud”. In the first five articles of the series we drilled down to Data Management and Data Quality as the basis for fraud detection analytics, to Business Rules and Watch lists
This is the fifth of the seven parts of blog post series “A practical guide to tackle auto insurance fraud”. In the first four articles of the series we drilled down to Data Management and Data Quality as the basis for fraud detection analytics, to Business Rules and Watch lists
In the first three articles of the series we drilled down to Data Management and Data Quality as the basis for fraud detection analytics, to Business Rules and Watch lists techniques that play always a crucial role for claim handlers and fraud investigators and to Advanced Analytics which add a
Criminal enterprises are tapping into the lucrative opioid business through creative schemes that are less likely to be identified as opioid abuse, misuse or diversion. One of the latest schemes? Auto insurance fraud. First, some background… While extensive progress has been made in establishing, improving, and mandating prescription drug monitoring
This is the third of the seven parts of blog post series “A practical guide to tackle auto insurance fraud”. In the first two articles of the series we drilled down to Data Management and Data Quality as the basis for insurance fraud detection analytics and also to the Business
This is the second of the seven parts of blog post series “A practical guide to tackle auto insurance fraud”. While Data Management and Data Quality are the basis for every analytical journey, and this becomes even more true for fraud detection analytics, the domain knowledge and business expertise plaid
Welcome to the 1st practical step for tackling auto insurance fraud with analytics. It is obvious why our first stop relates with data, the idiom “the devil is in the details” can easily be applied in the insurance fraud sector as “the devil is in the data”. This article analyses
I am more than glad to invite you to join me in a series of posts related to a practical guide for tackling auto insurance fraud in the new era of data science and advanced analytics. Insurers are used to face a constant threat, a powerful enemy that never rests.
The technology breakthrough during the last years have brought an increase in insurance fraud and, as a consequence, they are changing the landscape in the sector. From Pricing Comparison Websites (aggregators), to Telematics and Usage Based Insurance, to Internet of Things, the increasing demand for Cyber Insurance and new Peer
Using a standardized data model is an essential condition to achieve data governance in an enterprise. A standard data model supports data governance processes by implementing industry standards wherever possible: standards for contract and claims representation, mapping of data content with standard definitions (glossary function), use of code attributes
“All for one and one for all” is best known as the motto from “The Three Musketeers”, but this phrase could easily sum up the growing trend in social brokers. With advanced analytical techniques like generalized linear modeling insurance companies have created more granular pricing structures. But despite the assertions
Over the years I have written many blogs about insurance fraud including those on anti-money laundering, data quality in fraud, anti-fraud technology, life insurance fraud and even ghost broking. It’s clear that insurance fraud comes in many shapes and sizes and as losses continue to grow, detecting and preventing fraud
Insurance is a tough marketplace, but in many respects reinsurance is tougher! Today, the reinsurance industry is faced with an unprecedented number of challenges especially with what appears to be an increasing frequency and severity of man-made and natural catastrophes. To combat these challenges, reinsurers are turning to technology for
In my first blog article I explained that many insurance companies have implemented a standard data model as base for their business analytics data warehouse (DWH) solutions. But why should a standard data model be more appropriate than an individual one designed especially for a certain insurance company?
As I explained in Part 1 of this series, spelling my name wrong does bother me! However, life changes quickly at health insurance, healthcare and pharmaceutical companies. That said, taking unintegrated or cleansed data and propagating it to Hadoop may only help one issue. That would be the issue of getting the data
Does it upset you when you log onto your healthcare insurance portal and find that they spelled your name wrong, have your dependents listed incorrectly or your address is not correct? Well, it's definitely not a warm fuzzy feeling for me! After working for many years in the healthcare, pharmaceutical and
Nothing works today without an efficient data management – also in insurance business. A standard data model can be an important component of it. This article explains why. “Make or Buy”? This question has been raised very often by insurance companies planning to introduce a consistent data structure – a
In a recent blog I wrote about how big data is a game changer for the insurance industry. But the question that is often asked “What is big data”? Many people associate big data with the 4 V’s: Volume – The sheer size of data that is produced. Velocity –
What if a reckless driver adopted a more responsible approach because the car insurance pricing was based on driving habits? What if the senior from next door had the insurance payments based on kilometres driven, resulting in significant savings? This may be reality sooner than you think. The Internet of Things will revolutionise
What if a reckless driver adopted a more responsible approach because the car insurance pricing was based on driving habits? What if the senior from next door had the insurance payments based on kilometres driven, resulting in significant savings? This may be reality sooner than you think. The Internet of Things will revolutionise
The role of insurance is to bring some predictability, manageability and stability to a chaotic and uncertain world. In essence, it is a risk mitigation tool. The role of the Chief Risk Officer (CRO) is to manage the overall risk strategy for the insurance company. They are responsible for defining
Insurance can be a complex business, so filing an insurance claim can be daunting task for many small businesses. When an incident does occur, be it property damage, business interruption, professional indemnity or public liability among the myriad of other potential causes of loss, it is typically a period of
~ This article is co-authored by Binod Jha, Global Product Manager for Insurance Solutions at SAS, and Amol Kokane, Senior Development Manager for Insurance & Risk Management Solutions at SAS ~ How might insurance policies change if sensor data could be automatically transmitted and analyzed from your car, your home and even your
I cannot speak from experience, but predominately an Insurance CEO has three primary objectives: Grow the business Reduce expenses Ensure compliance. Let’s individually consider each of these objectives in more detail. Grow the Business How does an insurance company grow from a $2bn to a $3bn organization? Essentially, insurance has
What do the following have in common? A homeowner inflates the value of his home entertainment equipment stolen during a robbery. A parent states they are the primary driver for their child’s car. A doctor charges for a non-existent procedure. A construction company underreports payroll or misclassifies an employee’s duties.
Why do people have 12 pairs of shoes and no health insurance? - Perhaps the consumer price index data can help explain it ... Great minds think alike - I recently saw a plot of the Consumer Price Index (CPI) data that Layne Ainsworth had created, and it reminded me
Perhaps I should have titled this post, "Riddle me this Batman..." Bam! Zap! etc.... now back to our regular scheduled blog post, where we explore the many lessons that can be learned from one industry to the next. Let's start with one of the easier comparisons: How is an
