Smart Data Analysis for Financial Services
                                                              Mathias Bauer1

Abstract.1 This talk addresses opportunities for the application of     Data Analysis can (and should) play a central role at various stages
intelligent data analysis techniques at various stages of the value     of the value added chain in the financial industry. In the following
added chain for financial services. After introducing some basic        we will have a closer look at some relevant activities in this
notions and explaining the fundamental steps of data mining, we         context.
will have a closer look at various recent and ongoing projects and
discuss issues of practical relevance such as data quality and expert
knowledge. The talk concludes with some remarks on the potential        2.1       Appraisal of real economic goods
impact of new developments, e. g. in the context of Big Data.

                                                                        Scoring and rating processes are at the heart of financial industry.
                                                                        Here we will demonstrate an approach to appraise vessels as
                                                                        typical representatives of real economic goods which form an
                                                                        important class of investments.

                                                                        2.2       Fraud detection
                                                                        In B2B scenarios a company's annual accounts form the basis for
                                                                        their credit rating and all further negotiations. Usually, the numbers
                                                                        reported are accepted as a correct representation of last year's
                                                                        business activities. But what if they are manipulated? We describe
                                                                        an approach that identifies abnormalities in annual accounts, thus
                                                                        facilitating the detection of intentional manipulations.


                                                                        2.3       Identifying interesting customers
                                                                        There are numerous aspects that can make a customer particularly
                                                                        interesting to a company – his/her interest in certain products,
          Figure 1: The CRISP-DM process model for data mining.         credit-worthiness and default risk, churn rate etc. We describe an
                                                                        integrated approach to identify these individuals that reduces the
                                                                        marketing effort required while simultaneously improving the
1           DATA MINING                                                 company's insight into their customer base and the quality of
Data mining – this notion will be used as a synonym for all kinds       customer contact.
of smart data analysis – is a complex process that aims at turning         In particular, we will see how the modeling technique applied
raw data into actionable knowledge (see Figure 1 which depicts a        affects the usefulness of the analytical findings.
standard process model). We will introduce the basic notions,
discuss the various steps and in particular have a closer look at the
                                                                        2.4       Stock selection
choices to be made and a few pitfalls to be avoided.
   In particular, we will address the crucial aspects of how to         From an abstract point of view, selecting a relevant set of stocks is
choose an appropriate modeling approach and how to assess the           similar to the previous task as it mainly involves segmentation and
quality of a solution found by a data analyst.                          classification efforts. However, we will see that data preprocessing
   We show that in many cases it is not a good idea to simply           in this case is significantly more complex and requires some
apply the data analyst's favorite modeling technique. Instead we        advanced expert knowledge.
describe the various dimensions of such a choice and encourage the
end users of a data analysis to clearly state their requirements.
                                                                        3         Perspective
                                                                        Big data is more than a buzzword – even if it's not the silver bullet
2           SAMPLE APPLICATIONS                                         for all problems ahead. We will discuss various techniques and
                                                                        attempts to commercially make use of huge, largely unstructured
                                                                        data sets and briefly discuss potential future applications.
1
    mineway GmbH, Saarbrücken, Germany, email: mbauer@mineway.de