Nowadays we are able to process more and more data in our calculations. We can also give better insight in future risks, and in the relationship between various economic factors. In addition, we can draw more insight from the client data that is coming available. This gives us a wealth of data that has until now been hardly used. The challenge is to translate the multitude of big data to a customized advice for the individual situation.


This challenge applies to both private banks who provide clients on an individual basis with customized advice, and to financial providers who want to support large groups of retail clients. At this moment there are primarily two ways to advise clients.

1. The client receives advice from an expert

Experts have experience with a certain target group. This is why they can assess someone’s personal situation correctly and give sound advice, which they moreover can explain in a clear manner. This makes them both credible and effective. Unfortunately, such experts are relatively expensive and therefore only an option to a small, wealthy group of clients. An efficient advisory group will however increasingly help to decrease costs.

2. Learning from the peer group

Retail banks look at the type of decisions that comparable groups of clients take. This happens based on the data coming from an as large as possible peer group. The advice to follow the example of the majority of people looks similar to what comparison sites do.

Interpreting big data

Big data works well with products that everyone can grasp and understand. But financial decisions are often complicated. Furthermore individual situations often differ too much to be put under the same umbrella.

Example

Suppose that the data indicate that the majority of people will start saving extra if they know they have a pension deficit. It also turns out that people from a certain income group will save on average 200 Euro per month. Therefore you can advise people from that group who have not undertaken any action yet, to also go and save 200 Euro per month. But what you really want to achieve is offer clients options that fit with what they really need. Suppose that from a more detailed analysis of our example it shows that while people with a pension deficit are generally saving 200 Euros per month, but that it really should be 500 Euro. Do you then advise clients who have not been saving yet to put 200 Euro a month aside because the majority is doing so? Or do you advise clients to save the necessary 500 Euro.

The example shows that big data is not simply a load of information that you can use right away. You must always keep an eye on what you are looking at and take into account the personal situation of the client.

Helping with a change in behaviour

A proper use of data can help setting in motion a change in behaviour. If the peer group is performing well, you can of course perfectly use this as a motivation to make similar decisions as well. But when it turns out that the peer group appears to be making bad or unsound decisions, you must explain to the client what is going on, and what decisions are in his or her best interest.

From big data to customized advice

The challenge is to translate a multitude of big data to a customized advice for an individual situation. For many financial services providers this is an obstacle they are unable to take yet. But the time is gradually approaching at which this advice will be of similar quality to the advice of human experts. Because experts also increasingly get their knowledge from big data. However, they may still look at the wrong aspects and not give the objectively best advice available. With an automated advice that risk is a lot smaller. This can help to make the advisory process of the advisor more efficient and include more knowledge and information.

We will make a great leap ahead if we can make the automated advice more comprehensive. At this moment it usually limited to one goal, and regarding to one part of the wealth. But in the very near future this advice will become much broader and other issues will be incorporated such as the mortgage, the pension, investment and saving goals, study and college expenses for the children, healthcare etc. This will be a great development!

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