Nach Lust und Laune scheinen Versicherer die Jahresprämien ihrer Policen in schöner Regelmäßigkeit zu erhöhen. Wenn der zweimal gefalzte Brief so unschuldig im Kasten auf Öffnung wartet, möchte man am liebsten gar nicht reinschauen. Michael Rabin, Versicherungsexperte mit analytischem Weitblick, hat sich bereit erklärt, hier und jetzt eine Lanze für
Versicherungen arbeiten intensiv daran, ihre Geschäftsmodelle zu erneuern. Ein modernisiertes Aktuariat spielt dabei eine Schlüsselrolle. Warum? Das habe ich meinen Kollegen und ausgebildeten Aktuar Diego Rivas gefragt. Das Versicherungsgeschäft wirkt von außen wie ein langer, ruhiger Fluss. Trügt der Schein? Heute – eindeutig ja. Der Markt ist längst gesättigt, und
Unser Interviewpartner Marius Gödtel ist Leiter des Competence Centers Business Intelligence IT-Consulting bei der adesso AG, einem IT-Dienstleister im BI-Umfeld. Marius Gödtel ist ehemaliger Geschäftsführer der flitcon GmbH, die seit 01. Juli 2016 Teil der adesso AG ist. Herr Gödtel, wie präsentieren Sie die adesso AG auf dem SAS Forum in
Wirtschaftskriminelle Handlungen zu entdecken und zu verhindern, ist ein kontinuierlicher Prozess, dessen Optimierung der Versicherung finanzielle Vorteile verschafft und Reputationsschäden verhindert. Eine entscheidende Rolle spielen dabei vor allem die Prävention und eine optimierte Identifikation von Betrugsversuchen durch fortgeschrittene analytische Verfahren. Die meisten der im Betrug durch Vermittler anzutreffenden Handlungen manifestieren
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.
Versicherungen stehen massiv unter Druck. Negative Zinsen und ein hoher regulatorischer Druck führen auch nicht gerade zu Euphorie (versicherungswirtschaft-heute.de). Die Branche klagt, all das sei operativ gar nicht zu schaffen. Was tun? Niedrigzinsumfeld ändern? Geht nicht. Regulatorik beeinflussen? Geht nur partiell. Also bleibt nur, an Effizienz und Automatisierung von Prozessen
It's the age of big data and the internet of things (IoT), but how will that change things for insurance companies? Do insurers still need to consider classic data warehouse concepts based on a relational data model? Or will all relevant data be stored in big data structures and thus
Auditability and data quality are two of the most important demands on a data warehouse. Why? Because reliable data processes ensure the accuracy of your analytical applications and statistical reports. Using a standard data model enhances auditability and data quality of your data warehouse implementation for business analytics.
The Internet of Things (IoT) is drastically changing our lives, whether this is at home, in the car, at work or even in the street. Gartner has predicted that by 2020, 20.8 billion devices will be connected. Moreover, the potential economic impact of IoT by 2025 is estimated to be
Insurers are embracing digital to meet the demands of modern consumers. And, of course, there are obvious benefits to them from less costly, more streamlined interactions with their customers. The trouble is that digitisation comes with a major health warning: Unless insurers put suitable measures in place, they're at risk
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?
One of my colleagues often asks me “What’s new in insurance”. For an industry that is risk adverse, change does not come easily. In the past we have discussed innovations concerning telematics, drones, wearables devices and even weather data. However when he asked me last week and I responded that
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
How many of us have used the phrases… It’s a piece of cake Anyone can do it It’s as easy as ABC I could do it with my eyes shut When it comes to business intelligence it should be “easy peasy” but for many organization it can still be 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 –
A recent survey by Capgemini found that 78% of insurance executive interviewed cited big data analytics as the disruptive force that will have the biggest impact on the insurance industry. That’s the good news. The bad news is that unfortunately traditional data management strategies do not scale to effectively govern
The role of insurance is to bring some predictability, manageability and stability in what is in essence, a chaotic and uncertain world. So as we head into 2016, what are the big issues for insurers in the next 12 months? Below is just a selection of some of these issues:
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
Similar to claims fraud, money laundering is seen as a victimless crime, and often glamorized in movies and books. Think “The Wolf of Wall Street” and “Scarface”. But money laundering is a SERIOUS problem. According to a 2013 report, the United Nations Office on Drugs and Crime estimates that $1.6
It’s rather appropriate that the rock band Europe recorded the hit “The Final Countdown”, because today, September 22nd, represents 100 days until the much anticipated (and delayed) European insurance legislation Solvency II will come into effect on January 1st 2016. Designed to introduce a harmonized, EU-wide insurance regulation, Solvency II
Big Data has become a technology buzzword. But how is Big Data changing insurance? Historically, insurance companies have used SMALL data to make BIG decisions. Today, insurers are using BIG data for SMALL decisions. What does this mean? Traditionally, insurance companies have aggregated data to group risks into broad categories
“Garbage in, garbage out” is more than a catchphrase – it’s the unfortunate reality in many analytics initiatives. For most analytical applications, the biggest problem lies not in the predictive modeling, but in gathering and preparing data for analysis. When the analytics seems to be underperforming, the problem almost invariably
Oh, how times have changed during my 20-plus years in the insurance industry. Data wasn’t a word we used much back in the 80s and 90s, unless of course you worked in those arcane and mysterious IT data centres. Even amidst the computerisation of the insurance industry in the 80s, many
Oh, how times have changed during my 20-plus years in the insurance industry. Data wasn’t a word we used much back in the 80s and 90s, unless of course you worked in those arcane and mysterious IT data centres. Even amidst the computerisation of the insurance industry in the 80s, many
