How  advanced analytics can impact financial services

How advanced analytics can impact financial services

8 min read Sep 21, 2020

For the last few years, we have been talking about data in various superlatives and how it’s going to change our lives and the way we do business. As that idea is still present in 2020, it must mean that the trend is here to stay, and you can find the proof for it everywhere around you. Whether you buy suggested items on Amazon, or you see a relevant ad while surfing online, or you search for “Nearby” in Google Maps; there you go, you’ve just used a service that has data collection and advanced analytics included in its DNA.

The same thing is (fortunately) happening in the banking industry and other financial services are catching up too. Nowadays you might be living in a small, non-EU country and still have advanced mobile banking, access to online banks such as Revolut or N26 where you can set up account in 60 seconds, the ability to photograph your car accident and make a claim with your insurer, or to invest some money in stocks listed on the NASDAQ from the comfort of your couch as a small-scale retail investor.
In 2020, whether you are a retail bank, investment bank, broker, exchange, wealth manager, insurer, fintech company or even a central bank or regulator, data has a huge impact on your business in several segments.

Four key areas where data can add value in the financial services sector

Customer intelligence

The segment most visible to end users can be referred to as customer intelligence. Gathering data from various data sources such as mobile banking apps, social media, GPS movements (if the customer agrees to this), and blending it into machine learning algorithms for smart segmentation can open ideas beyond pure demographics on how to best position your products, and how to position them at the right moment to the right segments of your customer portfolio.

Well-designed products in this area could really change the perspective of how young generations see banks and insurance companies. For example, I would love it if someone were to take and analyse data from my Garmin watch, and then could provide me with health insurance offers according to how active I am and would allow me to accept/sign up for these via my mobile banking app.

Reimagine business processes

The second area of potential improvement is basically to reimagine business processes to boost efficiency in some business areas. This segment relies on data that companies already have, but are not using it in the right way or are not presenting it to the right people. For a sales rep to boost their performance, they need to have a few simple drill-down sheets on their BI tool on their mobile phone.

Forcing them to come to the office, export something and then create a pivot table out of it, does not boost performance. In all companies, all departments could make better use of the data they have with the right business intelligence tools, whether we speak of measuring call centre performance, boosting sales teams, or having a clear daily view of product profitability instead of a quarterly view.

New business opportunities

The third area which heavily relies on data analysis is often neglected in companies because it sometimes requires the blending of third-party data, but it can also lead to new business opportunities. For example, correlating the average deposit amount in your bank and average purchasing power by geographical regions can give a clear insight into which region is under-utilised or maybe requires more education on savings. Also, there may be a whole segment of potential customers sniffing around your website who just come and go, and by learning from their behaviours on your website, you can easily optimise the path so that, for example, buying travel insurance takes 2 steps instead of 6 steps, which would lead directly to new customers in your portfolio.

Risk management

The last segment is probably in first place in many corporate presentations, and this would be the segment of risk management. This is the area where companies are well developed in terms of delivering various simulations and checking credit quality or just, in general, maintaining compliance. However, as the amounts of data grow, so does the challenge of analysing that data and spotting fraudulent transactions, for example.

A few months ago, I received notification from my on-line bank that at that moment they had recorded a transaction which seemed suspicious and asked me to confirm it. I said “No, it’s not me”, and they said “Ok, your card will now be deactivated”. It took me another 30 seconds to create a new card and to add it to Apple Pay and then continue with my vacation. This means that this segment will have to continue to develop linearly alongside with data growth in order to keep the financial services industry on the safe side.

Three technology areas that are essential for any financial industry company in the next three years

Obviously, to support this broad number of use cases, companies have a wide range of technologies deployed. For the purpose of this blog, I will describe three different technology areas, corresponding to conclusions given by McKinsey, which we think will have a huge impact on how the financial industry is going to be doing business in the following years.

Modern cloud integration tools

Data business and analytics in general is not an area where you can buy an out-of-the-box tool and solve your problems, it’s more of a platform approach where you enable various users by giving them the right data at the right moment.
In large, heterogeneous organisations, this approach starts with the deployment of a modern and autonomous data warehouse and/or data lake, possibly by streaming data into a cloud. Appetites and needs in these large organisations can change quickly, and the only way you are able to answer those changing requirements on a daily basis is by quickly and proactively gathering data in a reusable repository while maintaining high data quality and data governance.

Looking from the perspective of the financial industry, this job becomes even more tricky because most (if not all) of them already have large sums of money invested in traditional DWH, OLAP cubes etc. which will require some redesigning in the following years. They will still remain here to serve some basic reporting or compliance reporting, but will not be able to tackle all the requirements that modern finance brings.

Self-service analytics & data literacy

Looking into all three segments that we are discussing here, this one has the potential of being utilised quickly. The concept of business intelligence is quite old now, but the technologies have evolved a lot. Many companies do have a portal or an application where users can see or download some static PDF or Excel report. And the usual thing they do with it is to join with another one, merge it in 50MB Excel files and create a pivot table in the end.
This is miles away from what any decision-maker should do today. Accessibility of self-service tools which do have strong governance on the other side has really evolved over the years. However, to get full usage out of them, people must jump out of that relational view of data and adopt the more flexible analytics that these tools offer. Changing such habits after 5, 10 or 15 years is not easy, especially because the concept of reading and understanding data is quite new to our school systems.

Have you ever wondered why most users want to see some information in a table? It’s because it’s not easy to read it from a 3-dimensional scatter plot. Luckily many big vendors in this area are investing more and more in enabling users through data literacy programs, and also in expanding a solution itself with some advanced functions which make it easier for a user to make some conclusions they couldn’t make by just looking at a pivot table.

Artificial intelligence

AI has also been around for some time as a buzzword but has started to play an extremely important role in our businesses. The popularisation of cloud technology and the progress of AI integrations in everyday tools (among other factors) have really pushed many companies into at least testing and playing around with smart segmentation, personalised offers, churn prediction, early credit repayment simulations, etc.

However the biggest gap in this segment is not technology in our experience, but the fact that a company really needs a multi-department skilled team in order to deliver a successful AI project, because without the ability to interpret the results and do something with them, even the best model is not worth much.

Modern financials vs. Less-modern financials

All this being said, I think there are quite an interesting 3-5 years ahead for the banking and financial industry in general, from a data perspective. Why 3-5 years? Because if it takes any longer, it might be too late for some of them. One of the players in the disruption market of online banks, Revolut, has grown by six times in the last two years (in terms of the number of registered users) and it remains on that curve. You know how they are able to handle it, because they started online and are still 99% online (more or less), meaning they are as scalable as Amazon’s web shop offering or Microsoft’s Azure server hosting offering – scarily scalable.

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About the Author

Milan Listeš

Business Development Manager Data & AI