A Loan Management System Without Origination and Underwriting Doesn’t Cut It 

img_turnkley-lender_WHAT YOU NEED TO KNOW ABOUT IN-HOUSE CUSTOMER FINANCING & 4 BUSINESS TYPES THAT WILL BENEFIT FROM IT IN 2022-1920

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For many organizations on the precipice of digital transformation, the thorniest parts of in-house lending are the steps known as “origination” and “underwriting.”

Some may be tempted to add “loan management” — also called “loan servicing” and “loan collections” — to this shortlist, but insiders know the loan-management piece is pretty straightforward. Sure, handling schedules, rollovers, fees, grace periods, and other intricacies pose challenges if you’re stuck with outmoded technology, but they’re easily met using a robust software-as-a-service financing platform. 

Origination and underwriting, on the other hand, “can be difficult steps because they encompass all the key analysis and business decisions made with respect to loan applications,” according to Dmitry Voronenko, co-founder and CEO of TurnKey Lender, a top lending-platform-software provider. “Get these initial steps wrong, or take too long to reach conclusions, and your business will suffer — a fact that keeps untold numbers of businesses out of embedded lending in the first place,” he adds. “That’s both unfortunate and avoidable.

But first, wanted to check if you (or your staff) would like this condensed white paper on the things you have to consider when choosing a loan origination software vendor.

Trouble is, some businesses think they can simply buy the management piece and handle gathering and analyzing crucial data using spreadsheets and legacy-tech workarounds. 

That’s a mistake, and the market seems to know it. Reflecting a decided move toward integrated loan management, the global digital-lending platform market is expected to approach $20 billion by 2026 for a compound annual growth rate of 19.6% through the seven years prior.

Like trying to build a rocketship with baling wire and duct tape

Loan origination is a multi-step process. To start, loan applicants submit financial information such as bank balances, bill-payment history, credit card information, and sometimes even tax returns. Lenders use these origination inputs to determine eligibility and — if it’s deemed a “go” — assign the most appropriate interest rate to offset risks the lender takes for making the loan, a process known as underwriting. 

[related-solutions]

But it isn’t just a matter of achieving business viability by means of efficient origination and effective underwriting. It often happens that — just as different businesses serve specific client types (in terms of, say, age, affluence, occupation, and recreational interests — a one-size-fits-all approach doesn’t do the trick. Lenders need customizable application forms to take proper stock of would-be borrowers, and manual approaches are simply too slow and too error-prone to work for most organizations.

That’s where a specialist software provider like TurnKey Lender comes in. “We have loan origination and underwriting modules that are time-tested in more than 50 markets worldwide,” says Voronenko. “They can and do work as standalone solutions, but they work even better as parts of our end-to-end automated platform, best exemplified by our Unified Lending Management Solution.” TurnKey Lender’s modules are “extremely flexible, and they allow lenders to adjust application, scoring and decisioning functionalities in minutes from an intuitive platform.”

When a business of any size or type — retail or wholesale, B2C or B2B — offers its customers credit from a digital platform, the loan-management piece is relatively simple. The organization sends out due-date notifications and — with prior permission — charges the borrower’s bank account directly. It’s that easy. 

But for loan servicing and collections to be as straightforward, the origination and underwriting pieces have to be done right. Errors created at those stages and not caught beforehand can cause trouble down the line. It’s where you can really see lenders start getting bogged down with errors and delays that impair customer satisfaction. Ironically enough, however, these troubles are avoidable.

As mentioned, some organizations — from banks to non-profits and everything in between — plug into some loan-management-only software and think they’re off to the races.

The problem? Trying to cope with origination and underwriting with tools that weren’t expressly built for the purpose means they’ll soon be up to the eyeballs in unsifted data, challenging decisions, and missed opportunities.

Why interoperability is so important to digital lenders

Meanwhile, they’re committed to a loan management system paired with homemade origination and underwriting modules that may not integrate well with systems software, if at all. So they’re stuck fretting about incompatible updates, clashing data formats, errant calculations, system errors, and all the resultant delays.

Businesses looking for solutions to these problems have three choices. 

  1. Stop (or don’t start) providing credit 
  2. Build-in elaborate workarounds that require constant monitoring and frequent action to ensure ongoing compatibility, especially in light of software updates
  3. Migrate to an end-to-end software platform like TurnKey Lender’s

The best course should be obvious. Access to an end-to-end, in-house, turnkey lending platform with a highly intuitive interface is the best way to secure an environment where:

  • Information flows without friction
  • Actions are internally visible, unambiguous, and permanently recorded
  • If problems intrude, diagnosis is straightforward

In addition to these operational benefits, TurnKey Lender’s loan-origination module’s decision engine is fueled by artificial intelligence and machine learning. 

Unlocking the treasures hidden away in vast arrays of data 

Artificial intelligence is simulated human intelligence whereby machines and software programs can — to an extent — learn, reason, and perceive, though quotation marks around those terms might be appropriate. 

Machine learning describes functionality that draws inferences from its environment without being explicitly programmed to reach pre-set conclusions.

How artificial intelligence, machine learning, and related concepts help lenders make loans is best understood by comparing traditional lending practices with more up-to-date approaches.

In an old-school setup,  the creditworthiness of prospective borrowers was determined by scorecards. This approach has several advantages, including accuracy and ease of oversight. On the downside, scorecard methodologies simply can’t handle big-data inputs. That was fine when lenders were content to sort through a limited number of data sources for information on loan applicants — things like loan applications, the lender’s internal databases, and credit-bureau scores. But now there’s a flood of additional data sources on prospective borrowers, including social networks, mobile devices, payment systems, and web activity. 

Of course, these inputs are highly relevant in gauging the creditworthiness of would-be borrowers — information that, without AI, “would stay locked away in datasets too vast and unwieldy to get at without help from machine learning,” says TurnKey Lender’s Voronenko. “The lending industry has a big-data problem — and machine learning is the solution that opens the doors to new customers and better long-term outcomes.”

In this light, Voronenko adds, “a lender that isn’t focused on end-to-end integration with robust functionality isn’t just giving up on efficiency. They’re saying no to AI, machine learning, and subsequent innovations — a stance that makes little sense from a strategic viewpoint.”

 

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For many organizations on the precipice of digital transformation, the thorniest parts of in-house lending are the steps known as “origination” and “underwriting.”

Some may be tempted to add “loan management” — also called “loan servicing” and “loan collections” — to this shortlist, but insiders know the loan-management piece is pretty straightforward. Sure, handling schedules, rollovers, fees, grace periods, and other intricacies pose challenges if you’re stuck with outmoded technology, but they’re easily met using a robust software-as-a-service financing platform. 

Origination and underwriting, on the other hand, “can be difficult steps because they encompass all the key analysis and business decisions made with respect to loan applications,” according to Dmitry Voronenko, co-founder and CEO of TurnKey Lender, a top lending-platform-software provider. “Get these initial steps wrong, or take too long to reach conclusions, and your business will suffer — a fact that keeps untold numbers of businesses out of embedded lending in the first place,” he adds. “That’s both unfortunate and avoidable.

But first, wanted to check if you (or your staff) would like this condensed white paper on the things you have to consider when choosing a loan origination software vendor.

Trouble is, some businesses think they can simply buy the management piece and handle gathering and analyzing crucial data using spreadsheets and legacy-tech workarounds. 

That’s a mistake, and the market seems to know it. Reflecting a decided move toward integrated loan management, the global digital-lending platform market is expected to approach $20 billion by 2026 for a compound annual growth rate of 19.6% through the seven years prior.

Like trying to build a rocketship with baling wire and duct tape

Loan origination is a multi-step process. To start, loan applicants submit financial information such as bank balances, bill-payment history, credit card information, and sometimes even tax returns. Lenders use these origination inputs to determine eligibility and — if it’s deemed a “go” — assign the most appropriate interest rate to offset risks the lender takes for making the loan, a process known as underwriting. 

[related-solutions]

But it isn’t just a matter of achieving business viability by means of efficient origination and effective underwriting. It often happens that — just as different businesses serve specific client types (in terms of, say, age, affluence, occupation, and recreational interests — a one-size-fits-all approach doesn’t do the trick. Lenders need customizable application forms to take proper stock of would-be borrowers, and manual approaches are simply too slow and too error-prone to work for most organizations.

That’s where a specialist software provider like TurnKey Lender comes in. “We have loan origination and underwriting modules that are time-tested in more than 50 markets worldwide,” says Voronenko. “They can and do work as standalone solutions, but they work even better as parts of our end-to-end automated platform, best exemplified by our Unified Lending Management Solution.” TurnKey Lender’s modules are “extremely flexible, and they allow lenders to adjust application, scoring and decisioning functionalities in minutes from an intuitive platform.”

When a business of any size or type — retail or wholesale, B2C or B2B — offers its customers credit from a digital platform, the loan-management piece is relatively simple. The organization sends out due-date notifications and — with prior permission — charges the borrower’s bank account directly. It’s that easy. 

But for loan servicing and collections to be as straightforward, the origination and underwriting pieces have to be done right. Errors created at those stages and not caught beforehand can cause trouble down the line. It’s where you can really see lenders start getting bogged down with errors and delays that impair customer satisfaction. Ironically enough, however, these troubles are avoidable.

As mentioned, some organizations — from banks to non-profits and everything in between — plug into some loan-management-only software and think they’re off to the races.

The problem? Trying to cope with origination and underwriting with tools that weren’t expressly built for the purpose means they’ll soon be up to the eyeballs in unsifted data, challenging decisions, and missed opportunities.

Why interoperability is so important to digital lenders

Meanwhile, they’re committed to a loan management system paired with homemade origination and underwriting modules that may not integrate well with systems software, if at all. So they’re stuck fretting about incompatible updates, clashing data formats, errant calculations, system errors, and all the resultant delays.

Businesses looking for solutions to these problems have three choices. 

  1. Stop (or don’t start) providing credit 
  2. Build-in elaborate workarounds that require constant monitoring and frequent action to ensure ongoing compatibility, especially in light of software updates
  3. Migrate to an end-to-end software platform like TurnKey Lender’s

The best course should be obvious. Access to an end-to-end, in-house, turnkey lending platform with a highly intuitive interface is the best way to secure an environment where:

  • Information flows without friction
  • Actions are internally visible, unambiguous, and permanently recorded
  • If problems intrude, diagnosis is straightforward

In addition to these operational benefits, TurnKey Lender’s loan-origination module’s decision engine is fueled by artificial intelligence and machine learning. 

Unlocking the treasures hidden away in vast arrays of data 

Artificial intelligence is simulated human intelligence whereby machines and software programs can — to an extent — learn, reason, and perceive, though quotation marks around those terms might be appropriate. 

Machine learning describes functionality that draws inferences from its environment without being explicitly programmed to reach pre-set conclusions.

How artificial intelligence, machine learning, and related concepts help lenders make loans is best understood by comparing traditional lending practices with more up-to-date approaches.

In an old-school setup,  the creditworthiness of prospective borrowers was determined by scorecards. This approach has several advantages, including accuracy and ease of oversight. On the downside, scorecard methodologies simply can’t handle big-data inputs. That was fine when lenders were content to sort through a limited number of data sources for information on loan applicants — things like loan applications, the lender’s internal databases, and credit-bureau scores. But now there’s a flood of additional data sources on prospective borrowers, including social networks, mobile devices, payment systems, and web activity. 

Of course, these inputs are highly relevant in gauging the creditworthiness of would-be borrowers — information that, without AI, “would stay locked away in datasets too vast and unwieldy to get at without help from machine learning,” says TurnKey Lender’s Voronenko. “The lending industry has a big-data problem — and machine learning is the solution that opens the doors to new customers and better long-term outcomes.”

In this light, Voronenko adds, “a lender that isn’t focused on end-to-end integration with robust functionality isn’t just giving up on efficiency. They’re saying no to AI, machine learning, and subsequent innovations — a stance that makes little sense from a strategic viewpoint.”

 

Share:

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DV interview blog article november 2023

How traditional finance providers can capitalize on the embedded lending revolution