Credit Unions have access to enormous amounts of data. Examining this data is invaluable in gaining insight about your organization. With competition in the financial services sector growing ever more intense, Credit Unions see the need to adopt a data-driven approach to stay competitive but quickly find that becoming a data driven organization is easier said than done. Here are some simple steps to making your data strategy a reality.
Trust Your Data
A study from the Harvard Business Review discovered that data quality is far worse than most organizations realize, saying that a mere 3% of the data quality scores in the study were rated as “acceptable.” This is problematic because low-quality data adversely impacts many areas of business performance. Poor data quality often results in a loss of confidence and abandonment of the project altogether. Continuing with a project driven by low quality data can translate into wasted efforts, increased spending and, overall, compromised decision-making.
Improving data quality and managing your data should be a top priority.
Manage Your Data
There are multiple steps involved with proper data management, as follows:
- Data collection: Loan data frequently originates from multiple disparate sources, such as core, real estate, and credit card processing systems. Gathering this data from multiple systems can be challenging but working with internal and external parties to aggregate this data will give you a more relevant view of your membership.
- Data storage: storage and/or staging of the data for further processing.
- Data manipulation: prepare the data to perform analysis more efficiently:
- Data filtering: Are sold loans included in your mortgage file? You better filter those our if you’re trying to agree to your balance sheet.
- Data cleansing: Are interest rates expressed as a number rather than a percentage? They need to be changed to percentages.
- Data transformation: Are addresses in a single field? They need to be separated so credit scores and property values can be obtained.
- Data aggregation: Do all your auto loans have the same loan identifier? They need to be consolidated into a single group for analysis.
- Data join or merge: Are the field names amongst the different system files the same? To merge the files the field names need to be changed and standardized.
- Data comparison: Have the results changed dramatically from the previous data set? Utilize prior data set to identify reasons for change.
- Data calculations: What are the weighted average interest rates or LTV’s within your portfolios? These need to be calculated.
Whew, are you worn out yet? If you do all of the above you have enough to generate a scorecard measuring key performance indicators and comparing past results to current results. This perspective into the past may provide insights but only a limited vision of the future. As the pace of change rapidly increases in the financial services industry it is imperative to be able to apply analytics to your data and begin to forecast the future.
Analyze Your Data
There are a variety of techniques to model and analyze data and combining of techniques is often used to get the best results. Analytics can be applied to discover hidden patterns, relationships, dependencies and unusual records or dependencies.
Predictive analytics can use a variety of techniques to determine the likelihood of future events from historical and current data patterns. Forecast analysis models are one of the most commonly used.
Forecast models handle value prediction by estimating the values of new data based on learnings from historical data. They are normally used to generate numerical values in historical data when there is none to be found. One of the greatest strengths of forecast models are their ability to input multiple parameters. For this reason, they are one of the most widely used predictive analytics models in use. This type of analysis can be used to determine future charge-off, COVID-19 portfolio impacts from deferred payments, credit card limit increases, or next product offerings.
Use Your Data
Lastly, you need to be able to visualize the data and to make actionable insights. Gained insights can be visualized using different methods like:
- Charts: A pie chart can illustrate the size of different loan portfolio segments.
- Graphs: Can show increase in real estate values over time.
- Traffic light indicators: Could be used to determine concentration risk for various portfolios.
- Heatmaps: Able to illustrate net interest performance by type of loan.
- Dashboards: A high level overview of important results and trends from an analysis.
- Actionable Reports: Are there higher than normal charge offs in a specific portfolio segment? This identifies a need for immediate action.
Credit Unions can use the insights they gain from data analytics to make informed decisions, leading to better outcomes. Analytics has come a long way in a relatively short period of time. It can assist in multiple aspects of operations and be a real game-changer for many businesses. But to get maximum results, companies need to know how to properly utilize technology, improve the quality of their data, effectively manage it, and use it to make better informed critical decisions. Those who are able to do so will have a considerable advantage over the competition and be poised to succeed in 2021 and beyond.
At 2020 Analytics we manage, analyze, and help you utilize your loan data, saving you time and resources. Our experienced team of analysts have a deep understanding of the challenge credit unions face during difficult economic times and how data can be leveraged to create a road map for success. During the Great Recession, we were able to help credit unions demonstrate to appropriate parties that they had the required capital to serve members and continue growing in a time of need and now we are performing analysis related to impacts of COVID-19 on portfolios. Contact 2020 Analytics to learn how we can help you analyze your data while understanding the risks and opportunities within your loan portfolio.