Performance Measures for Lending

9748041857?profile=RESIZE_400x 

Sanctioning a loan primarily depends on two major factors - the customer's ability and their intent to pay. The most accurate Information to analyze these two factors separates successful lenders from failures and increases customer acquisition. However, it is quite challenging to capture the right Information and get appropriate insights 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 better understand the Lending Analytics program, a workshop was conducted by CULytics in which Bob Little, Advisor at CULytics, and Naveen Jain, CEO at CULytics, discussed measures that must align with the lending strategies of an organization. The agenda of the session comprised.

  • The role of data analytics in the lending program
  • Practical examples of a few lending performance indicators
  • Best practices for implementing lending metrics programs to attain maximum outcomes

 Read out the summary of the session for more Information-

 

Optimize 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 data sources --from demographic data to transaction data to social media data- can be used by successful lenders to make lending decisions. For instance, with data analytics, lenders can analyze customer segmentation based on data sources, including debtor demographics, account activity, collections, and risk ratings, which help lending businesses 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. But 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. In addition, mobile app data analysis offers a continuous check on potential fraud scenarios even after a loan has been approved.

Thus, lenders can significantly lower their risk and take corrective actions faster by leveraging the power of data.

Performance metrics and why should we focus on them?

 9741739671?profile=RESIZE_710x

Performance metrics are figures and data consultants of an organization's actions, abilities, and standard quality. These can pinpoint the areas for improvement that will deliver the most significant ROI and impact profitability.

Common areas for lending performance metrics

KPIs are critical to Information on 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-

  1. Loan origination metrics
  2. Loan servicing metrics
  3. Default servicing metrics
  4. Financial performance metrics

 

 Loan origination metrics

It combines application, initiation, underwriting, closing, and funding

  • Average cycle time – (Sum of days from application to funding for all loans) / (# of loans funded in the same period)
  • Pull-through rate – (# of funded loans) / (# of applications submitted in the same period)
  • Average loan value – (Total loan volume originated) / (# of loans funded in the same period)
  • Cost per unit originated – (Total business expenses) / (# of loans funded in the 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 the same period)
  • Profit per loan – ((Total business revenue) – (Total business expense)) / (# of loans funded in the same period)
  • Abandoned loan rate – (# of approved applications not funded) / (# of approved applications in the 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

It combines payment processing, account maintenance, and 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

It combines 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 the 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 measures

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

 

Strategic goals

Good strategic goals can be made with a focused approach to the outcome. They are measurable on a sliding scale (e.g., increase x metrics from 10 to 25 or reduce y metrics from 70 to 55) instead of health metrics where the objective is to keep the metrics within a certain range. 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. Therefore, they should be short and engaging.
  • KPIsquantify the outcomes that are expected to achieve. They are measurable on a sliding scale (e.g., increase from x metrics from 10 to 25 or reduce y metrics 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. It is imperative to know which one is strategic and which one is important.

  • 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 enable to act after the fact, whereas Leading indicators help predict future behavior 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, measuring progress towards that outcome is essential 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 focus on departmental goals.

 

Remember, it's all about the outcome and not the actions. So try to avoid watermelon KPIs as they are green on the outside but red inside. Instead, make smaller changes for analyzing and enjoying growth.

  

Watch the full workshop herehttps://culytics.com/ppm-lending

 

 

 

E-mail me when people leave their comments –

You need to be a member of CULytics Community to add comments!

Join CULytics Community

 

advantedge
altair
ibi
arka
trellance
coopfs
dfa
wherescape
alkami
prismacampaigns
marquis
aiq
totex
cnet
datava
aun
cinch
know

Related Post

 

Ad Unit Settings






Ad Url Settings

 

api-lead-approach
the-amazon-lending-experience
executing-advanced-analytics-do-s-and-don-t
lending-transformation-old-vs-new
data-journey-building-strong-analytical-practices
4-step-iterative-process-building-a-relevant-analytics-practice
significant-measures-towards-new-normal
building-a-strong-analytics-practice-recipe-for-success
data-warehouse-evaluation-and-implementation
explainable-ai-trust-and-transparency
forecasting
top-50-members-using-transactional-website-jun-2020
top-50-cus-with-highest-and-lowest-efficiency-june-2020
importance-of-financial-risk-management
secret-sauce-for-long-term-sustainable-business-intelligence-succ
top-pfm-technologies
secret-sauce-for-long-term-sustainable-business-intelligence-succ
top-pfm-technologies
data-warehouse-and-bi-technologies-opportunities-challenges
top-chatbot-technologies
keys-to-building-an-effective-branch-or-atm-network
top-50-credit-unions-with-highest-and-lowest-accounts-per-member
lowest-and-highest-net-income-per-branch
marketing-holy-grail
top-50-most-and-least-delinquent-credit-unions
modern-marketing-technologies
incremental-low-cost-data-driven-wins
power-of-storytelling
the-cost-of-not-investing-in-data-governance
questions-you-should-ask-before-investing-in-data-warehouse
learnings-from-new-data-based-on-auto-loan-pricing
5-questions-you-need-to-ask-before-investing-in-data-governance
digital-marketing-maturity-models-for-credit-unions
marketing-expense-per-member
top-2-reasons-that-are-holding-credit-unions-back-when-they-are-i
using-data-analytics-to-manage-lending-complexity-while-driving-h
5-reasons-your-credit-union-should-invest-in-data-and-digital-now
top-50-most-and-least-efficient-credit-unions
retail-financial-services-outlook-during-covid-19
use-of-operational-analytics-to-mitigate-the-impact-of-covid-19
top-50-credit-unions-based-on-asset-size
cu-peer-comparison-dashboard
cu-peer-benchmark
all-about-machine-learning-engineering
top-web-design-trends
most-important-social-media-marketing-trends
state-of-digital-marketing-maturing-in-credit-unions
top-kpis-for-email-marketing
data-cloud-and-the-digital-transformation-imperative
digital-trinity-and-you
phases-of-financial-industry
analytics-roundtable-workshop
invitation-to-join-digital-transformation-hub
analytics-in-the-credit-union-business
value-of-member-centricity-and-analytics-in-the-growth-of-cus
all-about-membership-analytics
top-fraud-management-technologies
getting-started-with-your-data-analytics-journey
explore-vizualization-for-credit-unions
investment-in-website-personalization-technologies
data-analytics-supporting-cu-s-first-member-philosophy
loyalty-rewards-and-retention-technologies
member-experience-analytics
channel-analytics-and-its-importance
project-portfolio-management-technologies
investment-in-self-service-data-preparation-technologies
self-service-data-preparation-technologies
new-frontier-in-customer-experience-management
role-of-marketing-analytics-in-credit-unions
important-aspects-of-consumer-lending-analytics
kpis-on-website-analytics
journey-towards-bank-less-banking
investment-in-crm-technologies
top-omni-channel-vendors
conversational-banking-solutions
/top-kpis-for-chief-information-officer
mistakes-to-avoid-when-implementing-a-omnichannel-member
top-things-to-consider-when-building-dashboards
making-digital-marketing-more-agile-through-tag-managers
cecl-solution-providers
mistakes-to-avoid-while-implementing-marketing-automation
p2p-payment-integrated-solutions
kpis-for-social-media-tracking
kpis-for-human-resources-management
investment-in-fintechs-should-or-should-not
top-kpis-for-online-banking
investment-in-marketing-automation-technologies
investment-in-e-signature-technologies-should-or-should-not
tips-and-tricks-to-a-successful-bi-program
kpis-for-credit-card-business
kpis-for-digital-marketing
kpis-for-consumer-lending
hot-topics-for-credit-union-data-leaders
kpis-for-debt-collections
kpis-for-finance
website-personalization-tools
data-integration-technologies
robotic-process-automation-tools
why-data-analytics-initiatives-fail
electronic-signature-softwares
data-governance-tools-for-credit-unions
digital-and-mobile-banking-technologies
report-inconsistencies-are-frustrating
is-your-culture-ready-for-data-analytics
three-big-data-myths
turning-transaction-data-into-a-goldmine-a-becu-case-study
call-for-presentation-for-2019-credit-union-analytics-summit-is-n
top-10-keys-to-successful-data-analytics-practice
credit-union-chooses-accountscore-for-open-banking-transaction-da
how-much-do-you-spend-to-serve-a-customer
marketing-automation-technologies-for-credit-union
alexa-ask-first-abilene-fcu-for-my-balance
dataweb-content-management-technologies-for-credit-unions
efficiency-ratio
web-analytics-technologies
data-warehousing-software-for-banks
customer-experience-software
the-best-kept-secret-for-credit-union-data-analytics
mark-sievewright-on-technology-trends
naveen-jain-on-credit-union-analytics-summit-2018
why-analytics-doesn-t-make-a-difference-by-gary-angel
cuas2018-harnessing-the-right-data
build-a-financial-phone-assistant-for-your-credit-union-in-3-step
2018-culytics-analytics-challenge-winner
update-from-naveen
error-resolution
benefits-of-conversational-apps
who-are-your-most-valuable-members-part-1
how-alexa-can-help-your-credit-union
top-10-kpis-for-measuring-retail-channel-performance
how-much-is-too-much-personalization
top-10-kpis-for-measuring-contact-center-efficiency
pressure-on-margins-for-auto-loans-indirect-auto-loans-declining
best-business-intelligence-technologies-for-credit-unions
establishing-a-thriving-data-analytics-practice-is-a-journey
educational-presentations-from-the-2017-axfi-conference
modelling-alternatives-for-cecl-a-deep-future-analytics-study
data-analytics-use-cases-for-credit-unions-infographic
data-analytics-opportunities-in-credit-union-business
loan-application-analytics-with-cufx
machine-learning-delivers-great-consumer-experiences
deep-insights-of-credit-union-members-data-with-machine-learning
web-analytics-reporting-tips-for-credit-unions
big-data-strategy-roadmap-our-data-journey
webinar-framework-for-member-focused-decision-making
too-many-regulations-hurt-credit-union-members
digital-marketing-automation-solutions
online-banking-boom
transformation-transactions-to-relationships
top-dispute-management-technologies
2020-retail-trends
future-of-artificial-intelligence
2020-culytics-summit-attendee-dashboard
repositioning-the-role-of-marketing
marketing-automation-a-step-towards-marketing-transformation
strategic-agility
using-data-to-navigate-through-the-new-normal
digital-transformation-bcu
highest-and-lowest-new-loan-balances-per-branch-as-of-jun-2020
-new-members-ratio-as-of-june-2020
cus-with-highest-and-lowest-loan-grants-per-member-june-2020
self-service-data-preparation-technologies
highest-and-lowest-marketing-expense-per-member-june-2020
the-amazon-lending-experience
api-lead-approach
4-step-iterative-process-building-a-relevant-analytics-practice
data-journey-building-strong-analytical-practices
post-election-the-cu-outlook
most-and-least-delinquent-credit-unions-sept-2020
leveraging-ach-data-to-produce-real-outcomes
member-engagement-scores-benefits
member-engagement-key-to-serve-the-best
story-of-james-an-intelligence-transformation
executive-kpis-the-pulse-of-the-organization
untangling-member-journey
onboarding-strategy-to-deliver-success
the-importance-of-digital-technologies
top-interactive-financial-calculators
using-artificial-intelligence-to-improve-your-productivity
organizational-transformation-to-drive-growth
multi-year-journey-through-data-transformation
top-50-cus-with-the-highest-and-lowest-member-per-branch
digital-transformation-lessons-through-the-eyes-of-a-ceo
organizational-readiness-for-digital-transformation
ruthless-prioritization-to-do-more-to-learn-more-and-to-earn-more
performance-measures-for-digital-services
analytical-maturity-journey-towards-growth
less-is-more-the-necessity-of-focus-for-strategic-success
solving-the-crm-mrm-puzzle
insights-driven-messaging-member-and-product-onboarding
performance-measures-for-marketing
data-insights-that-drive-member-product-innovation
solving-the-crm-mrm-puzzle
the-agility-flywheel-a-strategy-that-never-goes-out-of-the-way
artificial-intelligence-as-a-playing-field-for-credit-unions
performance-measures-for-call-centers
top-automl-technologies
performance-measures-for-lending
building-business-case-for-data-analytics
driving-innovation-and-change
data-analyze-decide-and-create
digital-readiness-important-steps-to-achieve
digital-readiness-important-steps-to-achieve
enabling-credit-unions-with-ai
culytics-virtual-summit-2022-a-resounding-success
culytics-virtual-summit-2022-day-1
digital-banking-roundtable
digital-marketing-roundtable
transformative-lessons-from-a-chief-digital-officer
data-analytics-roundtable-mar-11
rewind-2022-culytics-day-key-highlights
data-analytics-team-roles
data-warehouse-development
data-analytics-team-size
is-your-data-analytics-program-not-delivering-results
active-deposit-management-for-profitable-growth
data-modeling
maximize-your-success-with-2023-CULytics-summit
biggest-opportunities-for-credit-unions
should-ceos-attend-the-culytics-summit
the-cost-of-a-wrong-decision
biggest-roadblocks-in-becoming-data-driven
a-journey-for-all-organizational-maturity-levels
maximize-your-data-analytics-checkup
navigating-the-data-analytics-landscape
improving-data-literacy
why-credit-union-leaders-should-invest-in-their-teams
why-credit-unions-should-not-invest-in-building-predictive-models
why-should-measure-the-success-of-data-analytics-program
cost-of-choosing-the-wrong-data-analytics-technology-stack
why-data-analytics-strategy-focus-on-supply-and-demand-side
kpis-to-measure-the-success-of-data-analytics-program
data-analytics-for-credit-union-branch-heads
data-organizing-principles
top-data-warehouse-storage-technologies
discover-the-hidden-truth-behind-watermelon-kpis
unveiling-the-hidden-dangers-of-cobra-effect-on-kpis
are-you-accurately-interpreting-your-kpi
unmasking-biases-a-guide-to-data-analysis-and-kpi-definition
uncover-the-power-of-proxy-kpis
unraveling-the-hidden-impact-of-sampling-bias-in-credit-unions
bi-department-structure
hidden-impact-of-confirmation-bias-in-credit-unions
getting-executive-attention-for-your-data-analytics-program
uncovering-biases-in-data-preprocessing
navigating-missing-data-in-credit-unions
navigating-sampling-bias-in-cu
unleash-the-power-of-real-time-data-use-cases
how-confirmation-bias-impacts-cus
breaking-down-selection-bias-in-credit-unions
unmasking-reporting-bias
elevate-your-cu-with-data-analytics-expertise
understanding-and-tackling-volunteer-bias-in-credit-unions
time-period-bias-in-credit-union
overcoming-biases-in-credit-unions
embracing-the-future-fast-future-fundamentals-program-equips-cred
unlock-growth-and-efficiency-credit-unions-guide-to-generative-ai