Data analytics transform unstructured data into actionable insights. The same techniques can also be applied in business. With the right technical support, analytics tools can help companies ease the pain of compliance and uncover untapped potential.
Banking is a case in point. Financial services organizations save vast quantities of data, which are often underutilized – or at worst completely untouched. By bringing data analytics and forecasting methods into play, the hope is to expose and exploit new efficiencies. One key motivation is the need to improve risk and price management – through more accurate credit scores, for example. Other incentives include a better customer experience and simpler compliance management. Companies can personalize offerings, identify new opportunities for cross-selling, and monitor customer churn. Driving up operational efficiency is also high on the wish list. Automation of manual processes and the dynamic scaling of resources can make this a reality.
These new data-driven insights enable enterprises to identify novel business models. Anonymized customer data can be monetized. And deeper understanding of customer behavior drives better market research.
The term ‘data mining’ refers to the transformation of raw data into useful information. The approach can be broken down into two sub-areas. On the one hand, insights can be gleaned from structured data such as transaction logs, activity profiles, or other database content. But thanks to current machine learning techniques such as Natural Language Processing and Deep Learning, unstructured data also has the potential to yield valuable information. This raw data could be anything from continuous text to images and video data. One typical application of data mining techniques is to check compliance in contracts, scanned documents, and e-mail correspondence. Another example is the use of sentiment analysis in social media. By analyzing texts and alternative data sources (such as social networks) based on a specific search term, it’s possible to identify positive or negative opinion.
Descriptive analytics deal with the analysis of historical data, with the aim of identifying patterns and relationships. Common approaches include cluster analysis and classification. Clustering techniques home in on structural similarities in databases, assigning each data record to a particular group, or cluster. The goal is to identify new ways of grouping data. Classification also assigns data records to specific groups. In contrast to cluster analysis, however, these groups are defined in advance. A good example of cluster analysis at work is in the activity-based segmentation of customer groups. These methods are supported by data visualizations, such as the presentation of cash flows in mesh networks.
As the name suggests, predictive analytics use historical data to predict future events. A mathematical model is generated based on historical data. This predictive model is then applied to current data in order to enable forecasting. Possible uses include fraud prevention, anti-abuse measures, and credit scoring, as well as predicting customer churn and price moverments.
Behavior-based customer segmentation (clustering)
A clustering approach was used to carry out behavior-based segmentation on pseudonymized customer transaction data. Based on the relative volume and value of transactions, customers were assigned to one of three categories – low, medium, or high.
Plausibility check of transactions (prediction)
Historical transaction data was used to create a predictive mathematical model. In this way, it’s possible to forecast the future volume of transactions. As soon as a transaction strays too far from its predictive model, an alarm is activated, and the transaction can be verified manually.
Extraction of key figures from company reports (data mining)
Data mining offers an automated alternative to manually scrutinizing financial reports, automatically extracting important figures from documents in PDF format. These figures can be called upon to aid decision-making in risk and return assessments.