The years 2020-2021 will go down in history as the years of uncertainty — and there’s hardly any sentiment more detrimental to the financial market than the common anxiety about the future. Luckily, the loan industry is rebounding after the decline. The global value of the lending market is projected to grow from $6.9 trillion to over $8.8 trillion in 2025, largely because people are more willing to borrow money as the world slowly recovers from the pandemic.
In the face of the increasing demand for their services, money lending businesses make every effort to seize the moment. Similar to other industries, also in their case, the key to success lies in harnessing the power of modern technologies such as automation, machine learning, big data analytics, and others. Let’s break down some of these innovations in banking software development to see how they can improve the loan experience for lenders and borrowers.
Lending software — what you need to know
Lending software (also known as loan software or loan management) is a set of solutions that automate and optimize the management of the lending cycle. Starting with origination, lending software assists financial institutions through credit scoring, refinancing, account processing, and servicing.
Consumer and mortgage banks, fintech companies, credit unions, and other organizations all use lending software automation to great effect, tapping into the following core advantages:
- Streamlining team workflows
- Eliminating red tape
- Reducing credit risk
- Simplifying procedures
Now, let’s explore some specific applications of lending software in the financial industry to understand how it can help your loan business better.
1. RPA for loan processing
In banking, repetitive back-office tasks take up the bulk of everyday work time, and they are particularly prone to mistakes. Additionally, the pandemic has exposed another flaw of these human-based lending processes: the incapability to handle a sudden surge in the volume of applications. When businesses worldwide turned to banks for a bailout, this limitation led to delays and disrupted the entire lending process.
Human error, tedium, and suboptimal efficiency can all be nullified using robotic process automation (RPA). A custom-made RPA system can quickly extract the appropriate information from applications and centralize borrowers’ data from all sources. Doing so frees up human employees and allows them to focus on tasks that are more complex or require empathy and an unconventional approach.
The two stages of the lending cycle that can particularly benefit from RPA are underwriting and validation. That’s because they involve the extraction and verification of sizeable amounts of data from physical documents, third-party institutions, and services. When done by humans, these tasks involve a huge time investment. Meanwhile, robotic systems can cope with them almost immediately and without costly errors.
2. Assessing creditworthiness with data and psychometrics
When evaluating a borrower’s creditworthiness, automated solutions can often be more permissible and benevolent than humans. That’s because banks often rely on credit bureaus for deciding whether the loan-taker will be able to service the payment. These institutions consider only the strictly defined requirements that applicants must fulfill, frequently obstructing a prospective borrower from receiving a positive assessment (and, consequently, the organization from winning a new customer).
Meanwhile, automated data processing allows financial organizations to evaluate more information in the same amount of time or even quicker. As a result, they can significantly broaden the parameters to include data such as the customer’s e-commerce transaction history.
What’s more, psychometric assessment adds a complementary personal angle to the scoring process. This method uses questionnaires or conversations to determine applicants’ traits of character like responsibility or carelessness. Then, they are compared with machine learning-driven response patterns to augment lenders’ decision-making based on more factors than just credit history, effectively making loan-taking much more accessible.
3. Multi-channel lending — loans for everyone
As a loan giver, you don’t want to exclude anyone from your customer base. Consequently, to make services accessible to the largest possible group, you need to ensure a variety of lending channels, including chat and mobile.
Here’s an example of a workflow that spans multiple channels of interaction with the borrowers:
- Your website will allow users to make the first step in the lending cycle with mobile-friendly design and online application forms.
- The mobile app enables quick and easy access to all documents and procedures to take the end-user convenience even further.
- Through point of sale (POS) lending, you allow your customers to request a loan and get a fast decision right from the store.
Additionally, you may think that phone calls and visiting the bank in person are things of the past. Sure, millennials, zoomers, and generations to follow are and will be getting increasingly mobile. However, baby boomers and Gen Xers still largely prefer human interaction when borrowing money, making call centers and brick-and-mortar branches still relevant. As there’s no single best answer to borrowers’ channel preferences, the best strategy is to serve your consumers across all touchpoints.
4. AI-based ID verification
Financial institutions and fraudsters find themselves in a constant arms race. To stay ahead, lending companies turn to advanced security tools like AI. One of the areas where artificial intelligence can truly show its potential is ID verification, a crucial process for lending.
Modern IDs are protected against tampering and falsification in multiple ways. They include polycarbonate sheets and see-through elements, as well as metalized holographic films. While indispensable, these features can make validation more difficult and laborious for human analysts — even more so when foreign documents are concerned.
Meanwhile, specialized AI-based verification solutions can distinguish a legit document from a fake one with pixel-perfect precision. Thanks to biometrics face recognition, AI can detect any attempts of face alteration, while file metadata extraction allows it to identify the tools used to manipulate the photo.
Besides, with machine learning algorithms powered by integrated global datasets, AI systems can recognize and analyze hundreds of document types regardless of where they have been issued. What’s more, they get more efficient at spotting fraudulent applications with every ID or any other document scanned.
Wrap-up
With soaring demand, the lending industry is currently both in an exciting and challenging place. To make the best of this situation, financial institutions need to embrace innovation and optimize lending procedures through RPA, data analytics, AI, and omnichannel unification.
