CISC 849 : Applications in Fintech Namami Shukla Dept of Computer & Information Sciences University of Delaware iCARE : A Framework for Big Data Based Banking Customer Analytics

CISC 849 : Applications in Fintech Big Data Extremely Large Datasets that may be analysed computationally to reveal patterns,trends and associations. Three major characteristics of Big Data.

CISC 849 : Applications in Fintech Every digital process and social media exchange produces it. Systems, sensors and mobile devices transmit it.

CISC 849 : Applications in Fintech Customer Analytics Give organizations the customer insight necessary to deliver offers that are anticipated, relevant and timely. Decrease attrition by accurately predicting customers most likely to leave. Developing the right proactive campaigns to retain them.

CISC 849 : Applications in Fintech Challenges To handle the massive amount of complex data in a cost- effective and efficient way. To effectively generate business value from the analytics and obtain competitive advantages for banks.

CISC 849 : Applications in Fintech Intelligent Customer Analytics for the Recognition and Exploration (iCARE) framework. It is presented to analyze banking customer behaviors from banking big data. IBM products - IBM SPSS* Analytic Server and IBM InfoSphere BigInsights* are used. Analytical models are Customized and validated on the processed data according to specified business scenarios iCARE Framework

CISC 849 : Applications in Fintech BigInsights platform is an Apache Hadoop based hardware- agnostic software platform. IBM SPSS Analytic Server (AS) provides big data analytics capabilities. It also includes integrated support for unstructured predictive analytics from the Hadoop environment.

CISC 849 : Applications in Fintech Architecture

CISC 849 : Applications in Fintech There are four phases in the solution: Data acquisition Data preparation Data modeling Business applications iCare Solution Design

CISC 849 : Applications in Fintech A standard input format is defined in iCARE for structured data. Unstructured data can originate from inside a bank, including web log files, call records and external resources. The unstructured data is usually stored as files rather than database tables. Data Acquisition

CISC 849 : Applications in Fintech To enhance the data quality both unstructured and structured data needs additional data preparation. Big Sql tool is used to efficiently handle the incomplete, incorrect, or irrelevant data. o Use of statistical methods Data integration Data Preparation

CISC 849 : Applications in Fintech Integrated data is stored in a data warehouse. Data Conflict is resolved. Consolidated enterprise customer view is generated.

CISC 849 : Applications in Fintech The iCARE analytical models can be built for different business scenarios based on business objectives. Advantages : All the statistic and machine learning methods are customized to suit corresponding business scenarios. Use of Parallelized models iCARE Analytical Models

CISC 849 : Applications in Fintech Example Traditional K-means clustering algorithm. To divide n data points into K clusters with similar points to minimize the total distance between the points to their cluster centers. Steps : The algorithm first randomly selects K data points to be the cluster centers. Then assigns each data point to the closest cluster, and updates the center of each cluster by calculating weighted average of all data points in the cluster.

CISC 849 : Applications in Fintech A customized and parallelized K-means clustering algorithm. It is used to segment customers of a bank into several clusters based on their profile and transaction information. K data points are selected to be the cluster centers. Steps: First data point is used as first cluster center. For each data point, compute the minimum distance between it and each defined cluster center.

CISC 849 : Applications in Fintech The Manhattan distance is used here. Use of manhattan distance. For two D-dimensional data points x and y, the metric is defined as New cluster center is selected. Each data point is assigned to the closest cluster using standard algorithm with distance metric. Updated cluster center :

CISC 849 : Applications in Fintech Data points are redistributed to their closest cluster Parallelized further to follow map reduce model. The distances between data points and cluster centers are updated by the Mapper. Reducer adds up the partial sum to get new cluster center.

CISC 849 : Applications in Fintech Business Applications Customer segmentation and preference analysis Potential customer identification Customer network analysis Market potential analysis Channel allocation and operation optimization

CISC 849 : Applications in Fintech Conclusion It can be extended for other data analytics applications, not limited to customer relationship management or the banking industry. iCARE framework is scalable by adding more parallel analytical models.

CISC 849 : Applications in Fintech 20 terabytes of data was analyzed to help generate insights for retaining active online banking customers. Implemented for commercial bank in southeast China Start-up

CISC 849 : Applications in Fintech The model ran 12 times faster as a single host for the 4 GB test data sample with 1,600 instances.

CISC 849 : Applications in Fintech Thank You !