Category: featured posts

Claims in insurance and machine learning

Claims are an important part of the insurance business. An insurer has the obligation to reimburse (or offer some other kind of ) any valid claim.

Therefore, it is imperative that insurers have a good understanding of the volume of claims they should expect to see in the near future. This is especially true for B2C insurance providers who might be facing a larger and more frequent number of claims compared to B2B providers.

The Tesseract Academy recently successfully completed a project with a world-leading electronics insurer provider. In this article, we will go through some of the things we did for them and how we went about them.

Forecasting claims: Problem specification

Our client was a leading electronics insurer. The business insures mobile devices against incidents such as accidental damage and theft.

This means that the business needs to keep devices in stock in case they are needed. But this is where the problem arises. If the company buys more devices than needed, it might end up spending more money than it should. If it doesn’t have enough devices in stock, then it might not be able to service the claim on time.

Therefore, the goal of our projects was to build a predictive modelling pipeline which could predict what would be the expected number of claims for each mobile device.

Forecasting claims: From statistical modelling to machine learning

We were faced with the challenge of forecasting over 1000 devices. The devices were released in different years, were in different parts of their lifecycle, and different specifications (such as memory, colour, size, etc.). This makes for a very complicated problem space.

In order to do that, we used a variety of techniques, such as:

1. Exponential smoothing
2. Kalman filters
3. Prophet
4. ARIMA
5. Tesseract’s custom AI forecaster

The combination of techniques ensures that we can cover any type of pattern, from simple patterns, to seasonal patterns, to trends unfolding overtime. Forecasting is a dark art, and we are masters of it.

Tesseract’s custom AI forecaster

One of the things we had the opportunity to use in this project to try out Tesseract Academy’s custom AI forecasting tool. This tool combines many different feature extraction approaches and machine learning algorithms, in order to create a unique forecasting method with excellent accuracy and generalisability.

One of the advantages of our method is that it allowed us to group all devices together. This means that the algorithm can learn all the different pattern variations that exist, and then detect whether a device is following one of those patterns. This makes forecasting much way more accurate.

One of the challenges that we faced is what is called “concept drift”. This is when the underlying pattern that is being studied changes. An example of such a pattern is shown in the image below. Our AI forecaster is able to accurately deal with situations like these, because it has seen similar patterns occurring in other devices, and has learned how to detect them.

Forecasting claims: Results and conclusion

When our project finished, we benchmarked our results against the predictions of human experts. We end up performing up to 50% better in many cases. This means that over the course of a year, a company can save close to £1million, by optimising its stock levels and supply chain operations, through the use of AI-based forecasts.

This is the real power of data science. When used appropriately, it can optimise operations, increase margins, and improve efficiency across the board. The initial cost of setting up the project cost a fraction of the total money saved. And the best part for our client is that the savings will keep accumulating over the years.

If you are intrigued by this case study and want to see how we could do the same for your company, then make sure to get in touch. We’ll be happy to help you.

Organisational culture is one of the most important topics in the post-covid world. The world of employment, especially in technology, is faced with many pressures, from new modes of working all the way to economic shifts. This means that keeping employees happy is key in attracting and retaining the best people, but also building impactful teams.

The world of data science in HR and organisational culture is growing as well. Data science can provide unique insights which otherwise be hidden. In this report we will examine how the Tesseract Academy data science team helped an organisation extract useful insights from HR data on sensitive topics such as gender and racial bias.

The problem: Data science, HR and bias

The Tesseract Team was commissioned to use data science in order to better understand how different demographics affect behaviours within an organisation. The client is a startup that is working with corporates who want to better understand the inner workings of their organisational culture.

The client provides questionnaires to the employees of corporates in order to examine questions such as:

1. Is there gender bias?
2. Is there racial bias?
3. Are there managerial issues?
4. Is there anything blocking progress?
5. Are there any departments that seem to operate better than others, and why?

The result of this process is a massive amount of data, with multiple answers from employees within many different functions. The client also has access to sensitive demographic data such as gender, age, and race.

The client is primarily interested in identifying biases, so the management team can them mitigate them or deal with them. This requires going through a large volume of complex data to find the right answer.

From a data point of view, the major challenge here is that there are multiple interactions and confounders, which might not always be visible at first sight. Therefore, before going to the management, the company needs to be 100% sure that a variable like gender or age really does affect behaviours (within the organisation) in a certain way.

The solution: Interpretable AI and machine learning

The Tesseract Academy data science team worked on a proprietary AI algorithm to achieve the desired outcome.

The algorithm performs the following:

1. It goes through all the questions, and fits a statistical model to measure the interaction between demographic variables, and the responses.
2. It then applies a machine learning model, to summarise all the statistical models applied in step 1.
3. Finally, it applies an interpretable AI methodology, in order to distill the effect of each demographic feature, such as age or gender.

The end result allows us to analyse any dataset within less than 30 minutes (even bigger ones), and distill all important factors.

The impact: Data science in HR and organisational culture

The impact of this exercise is clear. The client company can go back to its clients with a distilled summary of results and more confidence, than ever, that they are delivering the right results.

Since the client started working with the Tesseract Academy, they’ve experienced excellent growth, and is currently in the process of raising a big investment round, and closing some new big clients.

The Tesseract Academy is proud to support organisations of all sizes in their journey through AI, data science and blockchain. If you want to work with us and learn how we can help you, please get in touch. We will be very happy to speak with you!

Customer churn is a major problem in the insurance industry. The financial consequences of customer churn are huge and it is not something that insurance companies can ignore. It is important to understand the causes of customer churn, so that you can take steps to mitigate it.

Customer churn is a big problem for the insurance industry. This is because it is a lot more expensive to get new customers than to retain them. It is important for insurance companies to be able to identify which customers are likely to leave. They should then use this knowledge as an opportunity for customer retention by offering incentives or discounts in order to keep them from leaving.

We’ve already explained in the past how AI can be used to provide solutions in the insurance sector as well as fintech, and we explain more in our AI case studies bible (along with case studies from nearly 30 industries). We’re firm believers that data has the capacity to transform the way that the financial services industry conducts business.

The Tesseract Academy team worked with leading insurance provider based in London in order to predict and prevent churn through the use of machine learning and predictive analytics. In this post we explore this case study.

The problem: Churn, data and insurance

Our client is one of the biggest insurance companies in the space of electronic devices. The company resells policies to partners, who then provide these policies to their customers.

Customers might choose to buy insurance for their device for different reasons, a common one being that their device is new and expensive (e.g. a new iPhone). They also decide to leave for various reasons, such as a claim not being covered, or not really using the insurance.

With regards to customers leaving, there are two types of churn: active and passive.

Active churn is when someone cancels their policy before its expiration date. Passive churn exists when someone simply decides to not renew their policy.

Unfortunately, it wasn’t possible to get customer demographic data, due to data privacy regulations. However, we had data on things like:

• the device type and technical specs
• when the customer joined
• country

Plus some other proprietary datapoints.

Modelling customer churn using data science

There are various ways to model customer churn. Two popular ways to do it are to use survival modelling and classification.

A classification model can tell you the probability of a customer churning at a given point in time. The benefits of a classification model is that it is easy to interpret, since many classification models can provide you with a probability, which can be interpreted as a risk score. This means that the higher the probability of someone churning, the higher the risk.

A more effective and interesting class of models for this problem are called survival models. Survival models are popular in medicine, where they are used, as the name implies, to model survival of patients. A survival allows us to directly model relative risk (that is the risk of one customer vs another customer), and also model this risk over time. A example of this is shown in the figure below, where you can see how the risk is different for each contract type. A common characteristic amongst all three curves is that they tend to go down over time, indicating a lower probability of survival. What this means in simple terms is that as time goes by, the probability of quitting the service increases.

Predicting customer churn: The results

The conclusion of our work consisted of two core deliverables:

1. A determination of factors that contribute to churn.
2. A predictive model that predicts which customers are at higher risk of churn and when they are about to churn.

First of all, we were able to determine which factors place the customers at higher risk of churning and rank these factors accordingly.

Then, we used this knowledge to build a machine learning pipeline. Using this pipeline we were able to predict about 89% of the customers that churn with a precision of up to 90%, which means that we can target about 4 out of 5 customers that will churn.

Finally, using our model and domain knowledge we are able to rank customers according to when they are most likely to churn, so the business can prioritise contacting those at higher risk.

Predicting customer churn using AI: You can start now

It’s clear that predicting customer churn is a hugely valuable proposition for any company. The Tesseract Team has proven that it is possible to predict customer churn. While this particular case study is from the insurance industry, there is nothing preventing us from applying the same principles to other industries such as retail.

If you want to know more what we can do for your business, then make sure to get in touch. We specialise in delivering solutions in AI and data science, but also educating decision makers on topics such as data strategy and data maturity.