Guest editorial by Naveen Jain, CEO, CULytics
Sanctioning a loan primarily depends on two major factors: the customer's ability and their intent to pay. Having the most accurate information to analyze these two factors separates successful lenders from failures and results in an increase in customer acquisition. However, it is quite challenging to capture the right information in the absence of an appropriate mechanism.
The Lending Analytics program enables monetary establishments to make quicker and more informed lending decisions. It also aids in the efficient management of delinquencies and comprehensive loan servicing.
To gain a better understanding of the Lending Analytics program, a session was conducted by CULytics in which Bob Little, Advisor at CULytics along with Naveen Jain, CEO at CULytics, discussed measures that must align with the lending strategies of an organization. Topics covered included:
The role of data analytics in lending program
Practical examples of few lending performance indicators
Best practices for implementing lending metrics programs to attain maximum outcomes
Optimise Lending Decisions with Data Analytics
“Data is the New Oil”. This metaphor in the information age is a cornerstone for successful lending programs. Today, many sources of data --from demographic data to transaction data to social media data -- can be used by successful lenders for making lending decisions. For instance, with data analytics, the lenders can do customer segmentation analysis based on data sources including debtor demographics, account activity, collections, and risk ratings, which helps lending businesses to greatly increase their conversion rates.
Management of Delinquencies /Fraud
Sometimes borrowers who appear as the perfect candidate based on their past behaviors can show erratic payment and financial behavior once their loan is approved. This behavior jeopardizes the chances of full principal repayment along with interest, putting banks and other lending institutions in trouble. Delinquency prediction models, which use various data including past loans, transaction records, number of times a borrower had not paid in full, number of times they have gone way past the due date of payment etc., can mitigate that. Mobile app data analysis offers a continuous check on potential fraud scenarios even after a loan has been approved.
Thus, by leveraging the power of data, lenders can significantly lower their risk and take corrective actions faster.
Performance Metrics and Why We Should Focus on Them
Performance metrics are termed as figures and data consultants of an organization’s actions, abilities, and standard quality. These can pinpoint the areas for improvement that will deliver the biggest ROI and impact on profitability.
Common areas for lending performance metrics
KPIs are critical to information where the overall performance of your loan operation stands today, how it is trending, and what needs to change to be greater worthwhile or achieve other measures of organizational success.
Following are the key performance metrics:
Loan origination metrics
Loan servicing metrics
Default servicing metrics
Financial performance metrics
Loan Origination Metrics
These combine application, initiation, underwriting, closing, and funding:
Average cycle time – (Sum of days from application to funding for all loans) / (# of loans funded in same period)
Pull-through rate – (# of funded loans) / (# of applications submitted in same period)
Average loan value – (Total loan volume originated) / (# of loans funded in same period)
Cost per unit originated – (Total business expenses) / (# of loans funded in same period)
Application approval rate – (# of approved applications) / (# of submitted applications)
Incomplete application rate – (# of applications closed for incompleteness) / (# of applications received)
Fallout rate – (# of rate locked applications that don’t close) / (# of rate locked applications in same period)
Profit per loan – ((Total business revenue) – (Total business expense)) / (# of loans funded in same period)
Abandoned loan rate – (# of approved applications not funded) / (# of approved applications in same period)
Number of touchpoints: Consumer loan processing – (# of times staff must request Information from the borrower before the underwriting/credit operations function has all documentation required to approve or deny the loan)
Loan Servicing Metrics
These combine payment processing, account maintenance, escrow management.
Unit cost of loan servicing – (Total cost of servicing loans) / (Total # loans in servicing portfolio)
Servicing productivity – (Total # of loans in servicing portfolio) / (# of loan servicing employees)
Servicing issues per total loans serviced – (Total # servicing issues) / (Total # loans in servicing portfolio)
Cross sell and upsell – (Total Value of New Loans Sold to Existing Loan Customers) / (Total # Existing Loan Customers)
Response / Resolution time – (Total # of minutes required to complete a support task) / (Total # support tasks)
Payments processed per payment processing employee – (Total # of loan payments processed over set time) / (Total # of loan payment processing employees)
Payoffs processed per payment processing employee – (Total # of loan payoffs processed over set time) / (Total # of loan payment processing employees)
Default Servicing Metrics
These combine loss mitigation, collections, foreclosure, and repossession.
Successful loss mitigation completed per loss mitigation employees – (Total #r of loans successfully modified over a set time) / (Total # of loss mitigation employees)
90+ DPD loans as a percentage of loans serviced – (Total # of loans 90 or more DPD) / (Total # of loans), as a percentage
Non-performing loan ratio – (Total # of loans 90 or more DPD) with non-accrual status) / (Total # of loans at same point in time), as a percentage
Consumer loan charge-offs per consumer loan collector – (# of loans uncollectable over set time) / (Total # collectors)
Delinquent consumer loans per collections employee – (average # past due loans) / (Total # of collections employees)
Cycle time for debt recovery – (Time from start of collections to debt recovery) / (# successful debt recoveries)
Unit cost: Default servicing – (Total cost of servicing loans in default) / (Total # of loans in default)
Amount collected per collections employee – (Total dollar amount collected by the collections department over a set time) / (Total # of collections employees)
Financial Performance Metrics
It combines profitability, liquidity, solvency, efficiency, and valuation.
Total consumer lending expense – Total expense incurred by consumer lending over a set time from loan origination, processing and servicing
Total consumer loan revenue – Total revenue generated by consumer lending over a set time from loan origination, processing, and servicing
Consumer lending employee headcount ratio – (# of credit union employees) / (total number of consumer lending employees)
Consumer loans closed per channel/branch – (Total # consumer loans closed over set time) / (Total # channels/branches)
Average loan balance – (Total dollars outstanding debt) / (Total # loan accounts managed at the same point in time)
Average consumer loan value – Average value (in dollars) of a loan over a set time
Return on assets (ROA) – (Total dollar amount of net income) / (Total assets measured at the same point in time), as a percentage
Good strategic goals can be made with a focused approach to the outcome. They are measurable on a sliding scale (e.g., increase from 10 to 25 or reduce from 70 to 55). Goals are not the tactics used to deliver outcomes, programs, initiatives, or projects.
Strategies are about doing something different and increasing something. These depend upon the objective. Strategic objectives as measured by KPIs can be achieved by:
Strategic objectives are qualitative and memorable descriptions of what is required to achieve. They should be short and engaging.
KPIs quantify the outcomes that are expected to achieve. They are measurable on a sliding scale (e.g. increase from 10 to 25 or reduce from 70 to 55) over a period of time.
Activities, on the other hand, are the programs, initiatives, tasks, and projects associated with achieving Objectives. They are usually binary (done or not done).
KPIs vs Metrics
Before beginning to track these, it is important to understand the difference between these two. Which one is strategic and which one is important- it is imperative to know.
A metric is any standard of measurement. e.g.,:
1. Number of requests logged
2. Number of data owners identified
3. Percentage of requests resolved within SLA
A Key Performance Indicator is a quantifiable metric that drives improvement and that links to strategic business outcomes
A KPI is a metric, but a metric is not necessarily a KPI
Proxy metrics are an indirect way of measuring what is required to achieve.
Lagging indicators enables to act after the fact whereas Leading indicators help in predicting future behaviour and enable proactivity.
Balance, Quality and Efficiency
“For every metric, there should be a paired metric that addresses adverse consequences of the first metric. – Andy Grove
So, while trying to change some specific behavior, it might be backed by cost.
While working on achieving goals, it is important to measure progress towards that outcome to know that plans are performing as expected. So, remember that a good strategic goal focuses on an outcome. Also, Key Performance Indicators (KPIs) can be organization-wide or may focus on departmental goals.
Remember it’s all about the outcome and not the actions. Try to avoid watermelon KPIs as they are green on the outside but red inside. Make smaller changes for analysing and enjoying growth.
Check out this complete workshop on "Performance Measurements for Lending" and learn more about performance measures for digital services.