What Is an Unsecured Loan/Lending?
An unsecured loan is a loan that doesn’t require any type of collateral. Instead of relying on a borrower’s assets as security, lenders approve unsecured loans based on a borrower’s creditworthiness. Examples of unsecured loans include personal loans, student loans, and credit cards
- An unsecured loan is supported only by the borrower’s creditworthiness, rather than by any collateral, such as property or other assets.
- Unsecured loans are riskier than secured loans for lenders, so they require higher credit scores for approval.
- Credit cards, student loans, and personal loans are examples of unsecured loans.
- If a borrower defaults on an unsecured loan, the lender may commission a collection agency to collect the debt or take the borrower to court.
- Lenders can decide whether or not to approve an unsecured loan based on a borrower’s creditworthiness, but laws protect borrowers from discriminatory lending practices.
Types of Unsecured Loans
Challenges turning into opportunities in Unsecure Lending 2021
With debt levels rising continuously across the world, businesses have had to innovate to ensure that they can recover their money and remain viable. Today, the debt collection industry in the United States has a market size of $11.5 billion, according to IBIS World. However, traditional methods of recovering debt like invoice reminders, aggressive calls, or door-to-door collections, are proving to be unsuccessful. Inefficient collection processes based on poor strategies and imperfect models hike operating costs and also impact liquidity. According to a Collect Edge whitepaper, the industry average of delinquent debt collection rate stands at just 20%, compared to 30% a year ago.
Rescue via Digitization
Rescue via Digitization
A large number of delinquencies occur because companies do not track their customers effectively. Many of these defaults can be stymied if businesses can predict customers prone to non-payment and avoid these situations before they arise.
Machine learning and artificial intelligence make it possible to forecast delinquent customers as well as default rates. Such insights will allow businesses to create plans for specific clients including automated reminders and personalized repayment options. In the coming year, more and more debt collection agencies and software will leverage AI-backed technologies to take pre-emptive action to reduce their exposure to debt.
Technology-based debt collection will also lead to better client engagement. For obvious reasons, the debt collection industry is not a favorite among people. Apart from harassment from under trained professionals, sometimes the debt information is incorrect or even inflated. In fact, the Consumer Financial Protection Bureau said that in 2017, 39% of complaints in the US regarding debt collection were because customers were contacted for money, they did not owe in the first place.
AI’s predictive analysis will not only improve forecast numbers, but it will also be able to analyze client communication preferences and the extent of chasing that each one will require.
Our Strategy for Debt Collection Agencies
Constant economic changes are impacting consumer behavior significantly and traditional collection methods are insufficient to drive collections and manage customer experience.
We are chosen by lenders as our product allows to collect 10% or more using our proven AI Algorithms and a 3 Step New Collection Strategy that puts you ahead of other lenders in the minds of the consumer. This combination is a combination that provides lenders the maximum return.
Lender’s will learn about recent collection trends based on our large alternative data aka 140MM delinquency accounts and $45bn+ collected $$.
The beginning of the new decade will see a shift towards digitized practices and heavy reliance on technology. Collection strategies will also likely shift from aggressive methods to a more pre-emptive approach based on information and flexible payment options, making it a win-win for both parties. Infact, in 2020, the companies that embrace AI and machine learning with their debt collection strategy will know the best time to contact customers, tighten upon compliance by eliminating human error and reduce workload so the company can focus on the customer experience. The professional and methodical approach of machine learning will protect a company’s reputation too.