How to Face the Loan Decisioning Challenges of Today With Automation Solutions

To make it in today’s lending industry, your loan decisioning process has to be quick, low-risk, and accurate. It’s one of those crucial elements that either make or break a lending business. Decisioning can take days, rely on outdated rules, and scorecards or it can be powered by unorthodox data sources and analysis approaches and allow new-era lenders to compete and outperform large-scale rivals. The choice is yours. 

Luckily, FinTech in general and lending tech providers like TurnKey Lender, in particular, haven’t been wasting time. The times of old-school high-risk decisions fully reliant on traditional evaluation methods and human judgment are behind. But yet comparatively few market players have embraced the change for the best. 

Conservatism can be a good thing, but when it comes to getting a competitive advantage in a fierce market like that of lending, innovative decisioning can be the one thing that ultimately helps your business succeed. 

Now is a fascinating time to have a lending business. For one thing, there are so many ways in which companies manage loan risks and decisioning automation. Tools and processes range from Excel spreadsheets and intuition to sophisticated proprietary enterprise solutions, to ready-made solutions powered by deep neural networks. But no one disputes that speed and accuracy of today’s decisions determine tomorrow’s success. To help you set up a robust decisioning framework we’ve identified the top three challenges to be addressed by the decision automation solution you end up using.

 

Which lending automation solution to choose: TurnKey Lender vs nCino and Cloudlending.

 

Enhancing business decisions with data from non-traditional sources

TurnKey Lender works with numerous alternative lenders in developing economies. Businesses there often don’t have access to reliable data about potential borrowers. So the borrower evaluation techniques used by large-scale banks in tier-1 countries simply do not apply. Neither in terms of volumes nor amounts of customer data available. 

“In markets and market sectors that are still developing, the challenge is to incorporate non-traditional data sources into traditional models, to still leverage the analytical power of decision automation systems without imposing the same level of data rigidity that is standard in the developed markets.“ — says Brendan le Grange, Research and Consulting Director at TransUnion.

Reaching and servicing people in financially underserved regions is one of the big technological challenges of today. There are over 2 billion people without access to quality credit products. For a business, that’s a huge market to tap into if you have a reliable way to weed out the people who won’t return loans. At the same time, once these people get access to loans at fair prices, they will be able to build their credit history and develop their businesses as well as the economies of their countries. 

With that in mind, TurnKey Lender’s platform comes with a fully adjustable scorecard which will evaluate borrowers in seconds based on the criteria which the lender considers important. The decisioning engine is powered by machine learning algorithms which will adjust to the specific borrower types of each business to approve more of the safe loans faster. For each new major jurisdiction, TurnKey Lender rolls out a specific country edition of the software to address the challenges of the lenders there and ease the local regulatory compliance. 

But we didn’t stop there and carried out a research that led us to create a brand-new app for quick, accurate, and simple evaluation of the potential borrowers on the go, with little to no data about them. The solution is a stand-alone app which can be integrated with our fully-fledged platform. 

It’s called TurnKey Lender Psychometrics. It uses in-depth psychological evaluation combined with the company’s cutting edge algorithms to give lenders an idea if each particular borrower is going to repay them on time, helping them gauge risks and make an informed decision about the interest rates. 

In a nutshell, potential borrowers install Psychometrics, and are prompted to take a test. We use its results to assess each user according to the following criteria:

  • Honesty;
  • Family relations;
  • Punctuality;
  • Responsibility; 
  • Money management and financial planning;
  • Self-esteem;
  • Confidence;
  • Trust;
  • Likelihood to take risks;
  • Organizational skills;
  • Consistency;
  • Thinking speed.

Other factors include reading speed, impulsiveness, the range of movements on the answer sliders, changing answers, and action patterns. 

We’d also like to draw your particular attention to the importance of social media sentiment analysis. Sourcing and analyzing data from social media channels allows you to see the real-time picture of the real life of the user. For instance, you can use data on social media activity to uncover suspicious actions and detect fraudsters; to enhance customer pre-screening, cross-selling, and more. For example, you can use social media analysis to boost customer retention. Using metrics on social media authority of each client you can target your “Superstar” customers during your promotional campaigns and thus maximize the outcome of your promotions.

It is important to join all the insights gained through non-traditional data sources with your own analytical models, and possibly traditional approaches and sources. Lending technology is moving full steam ahead and savvy lenders have to adjust and use new ways to evaluate borrowers and make decisions. Alternative credit providers can’t afford to rely on the same methods old-school banks do. The old way is too slow, risky, and inflexible.

 

Receiving the information in the best way

“Balancing accuracy (which frequently requires advanced, black-box methods) and transparency (which requires those methods to be understandable and explainable)” – this is one of the most complex challenges to be solved by decision automation systems, according to Gregory Piatetsky, a founder of KDD (Knowledge Discovery and Data mining conferences) and the President of KDnuggets, which provides analytics and data mining consulting.

Decision automation systems need to instantly identify which data insights are required by any given business role (servicing, origination, reporting, collections specialists). This radically streamlines users’ access to large quantities of data, thus empowering businesses to resolve potential problems in real-time or even before they arise. 

Big Data is in the same category of buzzwords as AI, being thrown around too often these days. Yes, your lending software provider may be able to collect a ton of potentially useful information. But unless you can analyze it at a glance within an intuitive interface – it has little to no use for your business. Choose the decision automation software that not only has the capacity to collect and store data but the one that actually analyzes it for you and helps you make decisions based on this data. 

 

Being able to act on data in real-time

Decision automation software must follow through with the results of analysis and actually transform it into profitable decisions. Today’s solutions have to be able to predict the best next action. 

Previously they have been operating on an if-then basis. An example of what the if-then statements have been replaced with is the inclusion of a champion/challenger testing approach into a decisioning strategy. In this case, a decision automation application is supplied with different strategies and selects the next best action based on how every strategy has performed in the past. 

At its modern state, artificial intelligence allows implementing a sophisticated self-learning mechanism. In TurnKey Lender’s decisioning engine this is realized through advanced proprietary deep neural networks that learn about the borrowers and adjust over time to help approve more of the right loans faster. 

How fast? The entire analysis may take as little as 30 seconds giving lenders an almost instant answer about the risks and recommended interest for each particular application. The loan processing decisioning may even be fully automated if the lender doesn’t want to involve an employee in the process. But if they do, the borrower is still able to receive the funds within minutes after applying. 

 

Insights-Comprehension-Action

Use decision automation applications in a way which can be encapsulated into the Insights-Comprehension-Action formula:

  • Insights: choose the decision automation system that embraces advanced analytics on every stage of the process, and can enhance existing facts with data from non-traditional sources.
  • Comprehension: ensure that all the insights are presented in a transparent way and that user interfaces are tailored according to the business roles in your organization.
  • Action: make sure that the system follows through the entire decisioning process and transforms the knowledge into actions. Take advantage of the self-learning capabilities of your system, instead of simply following rigid algorithms.

TurnKey Lender platform’s origination module and decisioning engine offer credit providers with a state-of-the-art customizable scorecard that allows for seamless, safe, and fast decision automation. 

For an in-depth guide to choosing the right all-in-one lending automation software for your business, check out this comprehensive piece. And to see just how much time and money it can save your business, feel free to opt in for a free trial and schedule a demo of the TurnKey Lender platform.