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Data Science in Finance
The financial sector is the engine of the real economy and its performance is essential for the growth of the global economy. Financial service portfolios are rich and diversified ranging from standardized financial products to individual financial plans.
The challenge is to manage a growing demand of individual needs with adequate service quality.
"The decisive factor is not the sheer amount of data, but how to use it."
Trust is the basis of financial services, but the times of strictly loyal customers are over. Nowadays, they are less likely to be loyal due to today‘s critical reporting on current events, the experience with global crises and increasing demands. The dissolution of customer loyalty is leading to greater competition and a more intense market.
In a branch which offers largely homogenous products, customer services and effective cost management have increasingly become distinctive features of competitiveness, especially in retail banking.
Modern marketing instruments and a stronger customer orientation help to ensure sustainable success.
Need for Information
Therefore, reliable customer information will be needed to convey a professional impression and gain the customers' trust.
Financial Services and Big Data
Motor insurance for the first car, property insurance for the first apartment or occupational disability insurance for the first job – the need for certain financial products is mainly impacted by a person‘s stage of life.
In times of intelligent analysis methods, the unprecedented amount of data promises to provide an enormous advantage in knowledge compared to other competitors.
The majority of Fortune 500 CEOs in financial business are convinced that big data enables for a better decision making.
The right offer for the right customer in the right place at the right time. You can realize this typical distribution ideal with the help of statistical association analysis. The purpose of market basket analysis is to find patterns and dependencies in purchase behavior data. Completed by other data sources such as demographic data or social media, it creates a 360° view of the customer which allows targeted sales approaches. Understand your customers and their needs to be able to establish long-term relationships with them.
Loan losses and fraud can have far-reaching consequences for your company, which should be avoided. Modern analytical methods for fraud detection enable you to determine the individual default risk of potential customers in advance.
By examining account movements as well as data from external sources, the potential damage can be estimated, enabling you to identify more suitable quotations for these customers.
Since 2014 the requirements for the liquidity coverage ratio (LCR) to cover the cash requirements of the financial companies have been tightened. These requirements are necessary but also unprofitable at the same time.
By consideration of relevant factors, predictive analytics supports the planning of expected in- and outflows. This ensures the evaluation of liquidity reserves in an optimal way.
Correct, complete and up-to-date data are essential for successful operative processes. Data quality is highly relevant for IT systems such as CRM or ERP systems. Moreover the quality of analytical evaluations depends on a high data Quality.
We support you
We can help you to transform your big data volume into reliable insights and concrete solutions so that you can benefit from a great advantage.We can support you by applying our broad analytical methodologies that can be adjusted individually to your requirements and provide added value to you.
We will gladly advise you about predictive analytics (predictive maintenance), data mining and optimization procedures providing you with a sound basis for your corporate planning and simplifying your occupational schemes. This will also enable you to optimize your general process and plan them more efficiently. With Results as a Service we offer a scalable framework for individual solutions based on available modules. Immediately available, target-aimed, integrated and easy to handle.
In our approved trainings on the powerful programming language R, we share our knowledge with you enabling you to convert big data into smart data on your own.