What is important for any Credit Union is Data Analytics Supporting Credit Unions Members First Philosophy, which was elaborated upon by Joe Hartzler in his session. The major challenges as known by everyone today is the Changing Business Model, Capital Preservation & Expense Management, Doing Business in a New Economy, Regulatory Changes, Risk & Fraud Management, Informed Consumers, Payments Transformation, Shifting Demographics, Technological Advances and Non-Traditional and New Entrants coming into the market. It is necessary to figure out alternatives to go on and make sure that CUs change with the changing technology but at the same time taking care of the members.
The number one goal is no longer about survival but about what to do to stay relevant. Digital Data Explosion has got a lot to do with this as the amount of data is doubling year over year. The Return on Investment based on the maturity of Business Intelligence within the CUs has four stages:
- Descriptive Analytics is about What Happened.
- Diagnostic Analytics is Why Did It Happen.
- Predictive Analytics is about What Will Happen.
- Prescriptive Analytics is Making It Happen.
The maturity progresses as we grow and develop data analytics.
Data also has some downfalls if not careful while handling it. One example of its positive side is big data hospitals. They effectively reduced the wasteful spending caused by duplicate patient records, identified patients who are most at the risk of needing further expensive procedures and cut down on patient readmissions. Data can be used for anything. CUs play an active role in financial wellness. Big data or small data done right can help deliver personalized, tangible help. We need data to compete. Effective Big Data Strategy can drive better business results and growth. CUs can no longer resist an investment in big data. Their greatest advantage is the trust their members have on them as their advisors.
Filene, a research institute, did a research on data-driven CU which requires appropriate technology, people and organizational framework. The importance of leadership as it plays an integral role and that resistance to data initiative can lead to cultural distrust of data. Their result was to avoid departmentalization, data has a three-prong journey as beginning, understanding, and decisions and the outcome and the 3V’s of Data. Volume is the size of the data, Velocity is how often we’re getting data and Variety is the kind of data. Conquering the 3V’s of data leads to success. Certain questions raised with regard to the three include
- Volume of data storage as to how much historical data is required, security of data and what is necessary and helpful.
- Velocity of data in respect of real-time or batch data and how can we get third party information.
- Variety of data in regards whether it is core structured or unstructured and the resource of data.
The key components of analytical strengths are Technology, People and Organization. The technology consists of quality, analytical tools used, data infrastructure and data management. People comprise of analytical skills, ability to generate insights that lead to outcomes, leadership & cultural attitudes that support and foster evidence-based decision making. The organization encompasses thoroughness of measurements in place, analysis processes defined and integrated into operational activities and data governance.
The different stages of analytical maturity from low to high are:
ANALYTICALLY IMPAIRED → LOCALIZED ANALYTICS → ANALYTICAL ASPIRATIONS → ANALYTICAL COMPANIES → ANALYTICAL COMPETITORS
Analytical Maturity is the lack of internal skill sets and capabilities being limited to the specific area in the organization. To overcome this, employees should be encouraged to trust and value information and knowledge. They should desire to know more about data and run with data analytics to increase up to that maturity stage. The basic of data is to move from muck work to focus on the business at hand. The spending of big data today is in machine learning and adding transactional variables to traditional scoring models.
The general findings with research involve product progression, improved data scoring and more precise data needs. The goal is to see what next best product to sell or Product Prediction and the Prediction of Member’s Life-Cycle through cluster analysis. The various possibilities that open up entail patterns of usage to shape delivery channel investment, spending habits, spending patterns, where do members contribute to providing engaging rewards that resonate. It is imperative to serve member financial goals through algorithmic underwriting and machine learning.
Algorithms for financial behaviors cover credit score education and tracking, credit monitoring and alerts, dynamic pre-qualified offers and personalized financial content. Algorithms achieve prediction of Member’s Credit Score based on their transaction data and Prediction of Member Credit Risk. It helps in improving delinquency predictions, especially within a specific credit band.
In the end, by focusing on data CUs can know better what their members’ needs are based on their actions. CUs can find ways to offer members an alternative and identify opportunities to collaborate with other CUs on common data needs or challenges. Data needs to be looked at to survive regardless of the size of the CU. They need to transform data insights into a competitive advantage over big banks that invest in the latest in data trends for efficiency gains. It all depends on how to use data. CU should always have member’s best interest in mind as the end result.