Loan portfolio and customer analysis serve several purposes in order to protect a financial institution’s bottom line and generate revenue. Failing to have the right type of analytics strategy in place can have costly consequences.
Traditional Data Aggregation Methodologies in Lending
Traditionally, many financial institutions would combine their disparate databases into spreadsheets in order to analyze their performance. Although all departments can benefit from the information in the analysis, it is common for different departments to have independent methods for obtaining and tracking client information. The result is information that is not centralized, is possibly out of date and that may not contain all necessary data points to do a thorough analysis.
Allowing each department to use their own data collection methodology limits a financial company’s ability to act in a timely manner in order to protect and generate revenues.
One of the most important factors in marketing is being able to personalize messages to your customers. A comprehensive marketing strategy for lenders should incorporate loan portfolio analysis data as well as demographic data, customer data, and deposit data. This type of data grouping allows financial institutions to cater messaging to each customer’s specific wants and needs based on the following:
- Product Holdings – The type and number of products each customer holds provides important information about cross-selling products. This data includes items such as loan applications the customer has applied for in the past, or data on their loan payment history.
- Life Stage – As a person changes life stages, their financial needs change as well. A new college grad has financial needs that are vastly different than a person who just got married. Understanding where your customers are in life can help to cater products that align with their current life stage.
- Relationship – Understanding a client’s current relationship with your financial institution is important. Has the customer needed to contact the financial institution to resolve an issue? If so, was the issue resolved? How often is the customer communicated with? Does the customer engage during those communication attempts?
If your current marketing strategy is not incorporating this type of data analysis, your company might be missing lost revenue opportunities because it is unable to capitalize on current client’s needs.
Managing Credit Risk
Identifying, measuring, and monitoring credit risk helps protect the organization’s bottom line. Because lending activity accounts for the majority of revenue for most credit unions and banks, addressing credit risk is of the utmost importance. The first step in minimizing credit risk is through application analytics. By analyzing trends and variances in your applications you’ll be able to minimize your credit risk by implementing better underwriting criteria and more sound loan policies. At a minimum your application analytics should:
- Analyze funded, approved and denied ratios by product type, credit score tier, and if you do indirect lending by dealership and how those ratios trend over time
- Look at a disposition report by multiple segments. This report should compare approved, approved-funded, approved-not funded, denials, withdrawn and percent of total applications.
- Run what-if scenarios testing how changes in credit scores, LTV’s, and DTI can be adjusted for better loans.
- Run fair lending analysis for disparate impact.
The second step in minimizing your credit risk is through loan portfolio analysis. In order to do this sufficiently, each financial organization must be able to evaluate at a minimum the following:
- The quality of each loan by collateral value, borrower credit risk, probability of default, expected return and loss given default.
- The concentration risk the institution has by credit tiers, product types, geography, interest rates, loan officers, collateral types, etc.
- Static pool analysis of loan groupings to better understand the effects of underwriting.
- Delinquency and Charge-off ratios, volumes and trends.
- Trends and migration in in data for items such as credit scores, LTV’s, new production, loan types, credit limits, loan rates/grades, and negative equity loans.
- Simulations on expected loss given default and CRE loans
- Fair lending analysis for disparate impact
Without access to updated analysis and accurate data, performing the analysis above will be difficult if not impossible. This can result in leadership making decisions that are based off of inaccurate information, which can potentially impact the organization’s bottom line for years.
Having the right data analysis system in place can allow your financial institution to maximize marketing efforts while reducing delinquencies. These two steps combine to increase revenues and strengthen a financial organization’s bottom line. This can be done while reducing the amount of time your team spends performing manual analysis. Contact the team at Visible Equity today to learn more about their loan portfolio management options.