Author: Pedro Ecija Serrano

Cancellation Insurance?

Last week I had the honour of delivering the first talk of the first InsurTech MeetUp in Dublin.  There was much more interest than anticipated and we were overwhelmed with the success.  It has set the bar very high and we hope the next events continue to grow the local InsurTech community.

My presentation was a suggestion for cancellation insurance, to help airlines meet the costs of flight disruption and provide assistance service to stranded passengers.


The Shape of Things to Come


Modern technologies are increasingly used in the insurance industry.  Cloud computing is becoming more common, the use of machine learning and artificial intelligence is spreading at a steady rate and insurers’ demand for data scientists is on the rise.  Not all markets show the same levels of adoption but they are all moving in this direction at their own speed.

Open source machine learning frameworks and inexpensive technology at everyone’s disposal are fuelling the rapid adoption of machine learning in insurance.

Success stories and inexpensive technology are making this possible.  There is easy access to artificial intelligence and advanced machine learning techniques.  Popular data science languages such as R and Python have many relevant open source libraries.  

Deep learning frameworks such as Theano, Keras or TensorFlow are also open source and free for anyone to use.  Cloud computing is cheap and accessible to all thanks to Amazon Web Services, Google Cloud, etc.

In addition, the current lack of data science talent has already encouraged a myriad of Masters programmes and online learning courses that will add to the pool of data scientists.  All of this is great news.

However, this democratization of data analytics and technology has unintended consequences.  When everyone has access to the most sophisticated models and best cloud computing services, these are no longer a differentiating factor among competing organisations.  In short: you will not have an edge over your competition because you make extensive use of deep learning, as your competitors can close this gap much more quickly than before.

Relevant data, not technology, will provide the next competitive advantage.

It is important to remember that even the most complex machine learning methods are still models and the old adage holds true: garbage in, garbage out.  We have already seen cases where flawed training data has led to flawed results, sometimes embarrassing.  It is inevitable that we will see more cases as the use of artificial intelligence becomes more common.

So, is it true then that data is the new oil?  The Economist certainly thinks so.  Just like with oil, the importance of data lies in what you can do with it rather than in raw data itself.  It needs to be cleaned, processed, packaged and relevant to your needs to be useful.  Therefore, you are not sitting on a pile of gold simply because you have large amounts of data unless you are prepared to do something with it and it is relevant to your goals.  Home insurance data might not be of much help if you are planning to sell motor insurance.  Unstructured data from thousands of claim forms is pointless unless you make it machine readable and amenable to use with machine learning algorithms.

Access to relevant data will be the competitive advantage in the near future and organisations would do well by start preparing now.  There is much publicly available data whose potential is still untapped (The Irish Census, Eurostat, etc.)  But work is required before this data can be helpful.  This takes time and learning the potential and limitations of this data will take time too.  You may want to start now.

The computer games industry is a good example where this has already happened. Valve’s Steam still enjoys a dominant position as a seller of PC games.  Selling computer games is a largely digital business that generates significant amounts of valuable data for developers and distributors.  Steam collects this data and uses it to gain insights.

That industry has seen many attempts to replicate this model.  GOG has succeeded in finding its place but other initiatives such as Electronic Arts’ Origin are still far away from being a serious competitor.  However, Origin is a strategic development for Electronic Arts, as it enables the company to gather its own data.

Everyone knows that data is important. Customers and regulators too.

We have all heard how important data is and everyone is prepared to act on it. Customers are becoming more aware of the data they provide and regulators are concerned with the uses companies will make of data.  Financial services are not escaping this trend.

The insurance industry in particular enjoys a poor reputation among customers. Insurers will find that acquiring more data is not as easy as it is for other companies. Policyholders do not trust insurers and are reluctant to share data with them, as they believe insurance companies will use it against them.  However, they happily share much with companies they trust such as Google or Facebook.  Insurers will have to find a way to address this issue.

In addition, data protection legislation is evolving to face the new challenges that data analytics and artificial intelligence bring.  The GDPR is a very important development but we have already seen other initiatives to regulate access to data and the use insurers make of it.

The rise of the data broker?

Will the need for relevant data lead to the creation of data brokers?  It might happen. Alternatively, companies with large amounts of data might commercialise the insights gained from that data rather than sell the data itself.  Companies that have focused on gathering industry relevant data will be well positioned.  Google is probably the first company to come to mind but each market is likely to have its own dominant player and these may look like LexisNexis in insurance or Crème Global in the food and cosmetic industries.


Talent, technology and the adoption of machine learning will continue to be important but they will stop being a competitive advantage in the next few years.  Instead, the strategic use of machine learning and access to well maintained, relevant data will be the key to win.

Image source: Pixabay

Analytics Breakfasts for Financial Services


The next analytics breakfast will be at 8am on 8th August at Bank of Ireland in Grand Canal Square.  This time I will speak briefly about using data analytics for operational risk management.  In the past, we have discussed interest rates modelling and customer life time value among other topics.

Sign up and join us if you find this interesting and would like to network with like minded individuals.

Image: Mike Kunz / Pixabay

Insurance Fraud Analytics


Policyholders in the UK and Ireland have been hit by a series of large price increases in motor and home insurance policies.  There are many factors driving these hikes and insurance fraud is one of them.

According to Insurance Confidential, fraud costs the Irish insurance industry €200 million every year and motor insurance policies are, on average, €50 more expensive because of it[1].  There is a similar impact in the UK market with an estimated addition of £50 to every insurance policy.  UK insurers uncovered fraudulent claims in 2014 worth more than £1.3 billion[2].

The problem is hardly unique to the UK and Ireland.  Of the 1.2 million claims AXA handled in Spain in 2015, 1.3% of them were fraudulent[3].  That is close to the Spanish average of 1.37%, up from 1.12% in 2014.  Some lines of business have seen a 15% annual increase in fraud with motor insurance being responsible for 80% of all the fraudulent claims discovered.

Fraud affects all business lines and different areas where customers interact with insurers;  from non-disclosure at the time of underwriting to making fraudulent claims.  It can be organised, with several individuals planning and working together, or it can be opportunistic.  Fortunately there are tools to help insurers detect these types of fraud.

A solution to non disclosure

Non-disclosure is not always done in bad faith and customers may simply not be aware of important issues that can be relevant.  Nevertheless, whether it is accidental or intentional, statistics have a way to identify potential cases where non-disclosure may be likely.  Probabilistic Graphical Models  and Bayesian Networks are useful tools which identify cases with statistically unusual combinations, recommending a closer inspection.

It is impossible to investigate all applications but these methods provide a list of cases the system suspects of not having disclosed everything.  Data analytics does a good job at spotting potential inconsistencies in application forms. It allows insurance companies to operate with more confidence, knowing that the risk they take is appropriately assessed and priced.  It also provides a better customer experience by reducing the number of claims rejected due to non-disclosure.

Those interested in how these statistical techniques work may want to check Applied AI’s blog[4] for a three part series on Probabilistic Graphical Models.  The last two entries[5][6] focus on insurance.

Preventing claims fraud

Regarding insurance claims, the vast majority are legitimate and insurers expect them to happen.  But a small proportion of fraudulent claims are generated by organised teams.  This has a disproportionate impact in the claims experience of insurers, causing huge unnecessary losses[7].  Because they involve several individuals, the best tool for the job is a network analysis (graph databases).

Network analysis is a powerful weapon when fighting organised fraud as it finds a way to show what fraudsters have in common[8]: for example they live in the same house or close together, work together, they are friends in social networks[9], etc.

There are cases however, where policyholders see the possibility of making a false or an exaggerated claim and decide to act on it.  This is not organised crime but opportunistic and network analysis may not be useful in spotting this.  More common data analytics techniques can be used here and the challenge posed by the rarity of these fraudulent claims can be overcome with some preparation.  Those interested in the mechanics may check Dr Peter Brennan’s work on the subject[10].

But what is really happening?

Researchers are investigating increasingly sophisticated techniques to detect fraud, some including image and voice recognition.  In addition, the Association of British Insurers announced plans to invest more than £11 million to fund an expansion of the Fraud Enforcement Department in the City of London Police[11].

Insurers are beginning to up their game in relation to fraud analytics.  AIG has developed an in-house system that identifies twice as many fraudulent claims than the tools provided by the leading vendors in the fraud analytics market[12].

Irish Health insurer VHI set up a fraud investigation unit that has recovered over €47 million since 2009.  Liam Downey, from VHI, said that “widespread use of data analytics had been very effective”[13].  Also in healthcare, the US company Optum provides data analytics based solutions to prevent fraud, waste, abuse and enhance payment integrity[14], helping their clients make significant savings.

It is clear is that the impact insurance fraud has in companies and customers is too large to ignore and data analytics is, so far, the best solution we have.  Insurers are adopting data analytics as a very effective tool in the fight against fraud.


Image source: Steve Buissinne / Pixabay















Analytics Breakfasts for Financial Services

coffee-386878_1280I organise breakfast meetings to discuss predictive analytics in financial services.

This is to facilitate contact between financial services and experts on predictive analytics.

It should be of interest if you work in banking, insurance or aviation finance and want to use data analytics in your organisation or would like to expand beyond its current use. Join us if you are short of ideas or do not know where to start.

We will share our experience, and discuss market updates, trends and best practice in an informal environment.

Bank of Ireland has kindly agreed to host these meetings at their innovation hub in Grand Canal Square.  Feel free to join us and sign up at the doodle below:

Image: Mike Kunz / Pixabay

Predictive Analytics for Customer Targeting – A Banking Telemarketing Example

The following is a presentation I gave at the Dublin R MeetUp group in Ireland on 20th April 2016.

Unfortunately there is no recording available but feel free to contact me if you have any questions about the slides.

If you find this interesting, you may also like Using Data Analytics to Increase Cross-selling