Financial analytics - building on strengths and guaranteeing security

from | 14 March 2019 | Basics

Financial analytics is a tool that banks and insurance companies can use to build on their old strengths in a currently highly competitive industry in order to offer their customers the highest level of security in the future. Read here which methods and prerequisites you need for this.

Hardly any other industry has been affected by the impact of digitalisation as much in recent years as the Banking and insurance sector. New competitors such as FinTechs or crowd-lending or crowd-investing platforms are in direct competition with the banks.

Alternative payment methods, mobile and online banking as well as Artificial intelligence and blockchain technology are challenging traditional Banking services and the Bank branch as Institution in question. The latter in turn had a direct impact on the distribution of insurance.

A study by the University of Potsdam in 2015 showed that banks and insurance companies in particular work too conservatively. Even today, holistic data science solutions are rather rare. 

Chart, representation of competitive advantages through data analytics

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Using the example of the Virtual assistant and the Artificial Coworker, we show how banks and insurance companies can benefit from AI.

The latest studies in this area show that banks and insurance companies are using these Challenges and have reacted with initial measures. The Digitisation  puts pressure on banks, through Innovations We still have to make great efforts in this area in order to ensure that the competitive to stay.

Banks and insurance companies will remain relevant in the future - with new business models, innovations and high standards

However, recent years have also shown that banks are not simply being replaced. Rather, with the help of innovations from the field of Financial Analytics, the Development of new business models and Cooperations retain existing customers and even acquire new ones with start-ups.

For banks and insurance companies, it is therefore crucial to further expand their own strengths and optimise their internal corporate structure - both of which can be achieved with Financial Analytics. Solutions from this area help companies to identify and better respond to customers' needs.

Data science offers banks the chance to build on their strengths

Compared to conventional calculations in the context of financial management and financial accounting, a tool based on data analysis such as financial analytics offers more possibilities. Through Modelling and Simulation can be used, for example, in the area of cost and result management to make accurate predictions about the development of a product or profitability.

Banks can thus play two decisive advantages over the new forms of banking: They can offer their customers High quality advice and service offers as well as a higher level of security offer. Studies show that both - the increased need for personal contact with an employee and the need for security - are central values for customers.

As a result, the demands on bank advisors on the one hand and on the technical infrastructure on the other are constantly increasing. In the context of a personal consultation, additional Financial Analytics Tools can be used. In this way, for example, an employee's trading experience can be supported and improved with the help of forecasting models.

How financial analytics helps banks and insurance companies to optimise processes

Compared to other companies, the internal processes and interrelationships within banks and insurance companies are very complex. Important key figures, calculation factors and framework conditions come from outside - for example from the national and international financial markets, the legislator, institutions such as the ECB and others.

Financial analytics enables flexible pricing models, better risk assessment and a better Planning of assets and investments. Developing new products, predicting their likelihood of sales and determining profitability is thus based on a reliable database.

The multi-layered contexts pose a particular challenge for the Data Science is a challenge. The more complex the processes involved are, the more important it is that the data is cleansed in advance and checked for up-to-dateness, relevance and plausibility.

Data quality is essential for data science - in our blog article you will find the 5 most important measures to increase data quality.

The preparation, which in any case accounts for a large proportion of a Data Science Project is particularly sensitive in the banking and insurance sector. Even the smallest errors or inaccuracies can have major consequences with real losses.

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Data science for banks also existed in the 1980s. The difference to today is that computing power has grown exponentially while the cost of technology has become lower and lower.

Another commonality with the past innovations in the Banking sector: In the past, as today, people often talk about the possible loss of jobs. But neither did the introduction of ATMs to mass redundancies, nor do modern Data science tools and -solutions.

Besides safety, today it is about being close to the customer

Today, it is not only important for banks and insurance companies to Profitability of their products in relation to costs, losses, turnover and profits. Rather, it is also about understanding the Default risk in loans and insurance and to offer the right products, at the right market price, in the right place to the right customers. This is achieved, for example, by designing and offering products and services individually. (Customer segmentation)

Calculation of risks can be determined much more precisely today and thus be reflected in the calculation. Such a rating can even be made on the basis of a few interactions of customers on the company's homepage.

Another way to increase customer participation and satisfaction is to use the so-called "Community Scoring". By involving customers in communities and evaluating their interactions, the service can be improved and the number of possible touchpoints increased. But also customer loyalty and the prediction or prevention of churn (Churn Prediction) are among the possible data science applications in this area.

Financial Analytics - An intelligent planning and management tool for banks and insurance companies

The concrete areas of application of financial analytics in banks and insurance companies are numerous: the projection of future financial scenarios leads to optimised planning of assets, investments and intelligent workforce management. Monitoring tools provide valuable insights that support the measurement of performance, budget and personnel cost control as well as personnel planning.

However, beyond such individual solutions, which often answer department-specific questions, it is important to give companies holistic to consider (Data Journey).

The overarching goal must be to evaluate data across departmental boundaries in order to improve overall economic performance. Artificial intelligence in the marketing industry and in the banking sector is thus the central instrument that can provide better and more informed Decision-making processes made possible. Only in this way can banks and insurance companies enjoy all the benefits.


Michaela Tiedemann

Michaela Tiedemann has been part of the Alexander Thamm GmbH team since the early start-up days. She has actively shaped the development from a fast-moving, spontaneous start-up to a successful company. With the founding of her own family, a whole new chapter began for Michaela Tiedemann at the same time. Hanging up her job, however, was out of the question for the new mother. Instead, she developed a strategy to reconcile her job as Chief Marketing Officer with her role as a mother.

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