The Making of a Better Loan: Lending Tech Will Empower Borrowers, Reshape Lending

Debt is a significant and lasting factor in American’s personal balance sheets. Yet, until now few tools have been created to truly help consumers strategically manage the increasingly complex decisions about using credit.

Among Americans, 80 percent carry some type of debt, and the responsibility for those liabilities is continuing late in life. 1 In fact, Credit Sesame members hold nearly $107 billion in total debt across types such as loans and credit cards. 2 Of Credit Sesame members in their 60s, nearly 70 percent still carry credit card debt and 11 percent have outstanding student loans.

Managed effectively, the use of credit can be an indispensable for building wealth. Managed poorly, debt can burden families for generations.

Luckily, advances in technology – including data mining, deep learning and machine intelligence — make it increasingly possible for consumers to have access to robust resources for getting impartial advice, modeling scenarios, assisting with decision making and comparing products. These advances can help borrowers navigate today’s credit world and empower them by putting their needs first, creating better matches, transparency of information and more.

Lending Tech: Time to shift the focus from institution to borrower

Despite new technology being applied to lending over the past number of years, not as much effort yet has centered on helping borrowers. The industry has focused inwardly on streamlining origination and back office workflows – as well as more recently using microservices and process automation to leverage legacy systems, while preparing for future transitions. Although borrowers may have benefited from shorter processing time and in some cases e-signature ability, technology has primarily benefited lenders.

Now is the time for recent artificial intelligence technology advances to be put to work for borrowers’ benefit — in the form of a robo-advisor for the liability side of consumers’ personal balance sheets.

Today, robo-advisor models are increasingly common and are being used to handle consumers’ investment assets. In fact, there are nearly 100 robo-advisors in 15 countries. 3 These robo-advisors are expected to manage between $2.2 trillion to $3.7 trillion in investment assets by 2020.

This type of model also can be applied to helping consumers manage their debt. Robo-advisors for liabilities can help borrowers address needs for informed choice, transparency, education and overall better strategies for managing the cost of borrowing. With $12.84 trillion in total U.S. household debt as of Q2 2017 4 , the need is huge.

Institutions will also benefit. The machine learning of robo-advisor models that recognize hidden patterns in data will enable more automated and effective underwriting. Institutions will be able to create an expanded pool of potential clients that might not be recognized through basic FICO® score and income analysis. The robo-advisor model can even enhance marketing for lenders by identifying when consumers would most benefit from a particular offer.

All of these benefits may come at a cost for some lenders. Aggregation and transparency from debt robo-advisor model is likely to put pressure on lender fees and generally disrupt the lending market in favor of borrowers. That’s what we saw, as investment robo-advisors upended the securities brokerage business and platforms like Esurance, and reshaped the consumer insurance market.

Rise of the Lending Tech Robo-Advisors

So, what exactly does a robo-advisor for liabilities look like for a consumer? A robo model uses algorithms, deep learning and machine intelligence algorithms to put massive amounts of data and adaptive business logic to work to deliver value in three key areas:

Assessment

A robo-advisor for liabilities provides consumers with a much more robust picture of his or her capacity for borrowing – far beyond a simple debt-to- income ratio. Through gathering and analysis of traditional and non-traditional profile information, the debt robo-advisor can show the consumer a range of options available by weighing other features, increasingly considered by lenders, that establish creditworthiness.

The robo-advisor can also perform complex calculations, simulate scenarios and trade-offs to show consumers their maximum borrowing available per credit product type or through debt portfolio reallocation.

Aggregation

With the consumer’s profile and borrowing power understood, the robo-advisor can pull together a myriad of lending product offers, displaying a range of options ranked according to each individual’s situation.

The consumer then can make an informed choice, seeing recommendations ranked for best fit by the robo-advisor’s algorithm. Or the consumer might choose to sort and compare options by a particular variable, which is possible because the robo-advisor has parsed and displayed details of the products in a transparent way.

Machine learning will also come into play as the consumer gives feedback on the importance of different product features and terms such as monthly payment amount, rate variability and length of loan.

Recommendation

Robo-advisors for liabilities also have the potential to help consumers better manage and optimize existing credit lines and loans by recommending high impact actions and tracking results over time.

This loan optimizer functionality evaluates current loans and helps borrowers make strategic changes. The robo-advisor provides suggested refinancing or balance transfer strategies to effectively accomplish goals such as minimizing interest payments, lowering monthly payment obligations, shortening payoff periods or even maximizing rewards.

Help for Today’s Borrowers

This next generation of robo-advisors is exactly what consumers need to help them understand their options and make informed decisions efficiently. Consumers can gain a high degree of confidence from learning their true borrowing power and its most effective uses, as well as from impartial advice on which options are the best fit.

Beyond basic product selection needs, robo-advisors for liabilities will be well positioned to advise clients on other critical aspects of liability management such as debt reallocation decisions, actions to strengthen credit scores and steps to maximize reward benefits. Overall, exciting artificial intelligence technologies are helping to create a new consumer era in lending and the making of better loans.

By Pejman Makhfi, CTO of Credit Sesame

Author: Dylan Jones

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