Building trust in Machine Learning and AI in digital lending
fraud prevention to investment predictions and marketing, Machine Learning (ML)
and Artificial Intelligence (AI) are recent cutting edge developments in the
finance industry. Particularly
in the digital lending space, the next step to truly integrate these
technologies is to build consumer trust in them.
trust barrier facing machine intelligence
the recent global study by Pegasystems, it was shown that only 35
percent of survey respondents felt comfortable with AI.
More specific to the
finance industry is HSBC’s Trust in Technology study, which found that only 7
percent of respondents would trust an AI to open a bank account, and 11 percent would trust an AI to dispense mortgage advice. Notably, the dominant concern was that AI cannot understand our needs as well
as another human being. There is a trust barrier, which is the challenge facing banks,
traditional financial institutions and fin-techs such as digital lenders.
the lack of empathy in AI
it opening a bank account or having loan applications screened by AI, consumers
are uncomfortable with having a machine “in charge”, despite the fact that an
AI could possibly reduce human bias and personal preferences in
granting loans and approving deals. First, digital lenders can
overcome the lack of empathy in AI by educating their consumers about how their
algorithm works and what the requirements are. For
example, exploring ways to increase algorithmic accountability, including the
possibility of having algorithms reviewed by a regulatory board.
communicating how AI is deployed to screen applicants is crucial.
example, by taking personal bias out of the equation, there are fewer chances
for people to take advantage of personal connections to get a loan approved.
With AI, applicants would be assessed based on their qualifications alone. It
is also important to be transparent and stringent in how funds are handled. For
instance, P2P lending platforms like Validus do not keep investor funds in
their own accounts.
are held in escrow until they are disbursed to borrowers. Digital lenders should
emphasize the due diligence required for handling monies, especially given that
they have less face-to-face interaction with customers.
the interpretability barrier in ML
The interpretability barrier is a long-standing issue in AI and ML. It refers to
how machine thinking can yield accurate results, but lack the ability to
explain them. This is a constant source of frustration to consumers, who are
disinclined to trust what they cannot understand. To
get past the interpretability barrier, it is important for digital lenders to
simply explain what their AI cannot, especially when the AI processes many
factors and data. For
example, to generalize complexities, the Credit Bureau of Singapore (CBS) can
use its credit scoring system to explain which factors contribute to bad
credit, even if exact numbers on how much impact different factors hold, cannot
and acknowledge the criticality
Criticality refers to the
degree of risk posed to the consumer should the AI make a mistake. The higher
the degree of criticality, the more important it is to prove the AI’s accuracy. Digital
lenders must acknowledge that their AI has a higher degree of criticality than
most consumer services like AIs tasked with recommending the next Netflix movie. If
a loan of US$300,000 is disbursed to a company that cannot repay it, the result
is significant financial damage to a digital lender and its investors. To
prove and acknowledge criticality, digital lenders should constantly
communicate the provability of its AI through benchmarking, repeated
simulations and backtesting. For
example, the digital lender’s default rate compared to the top three banks
should be included in regular reporting to investors.
confidence in ML and AI
ML and AI, financial institutions had to overcome significant distrust when
algorithmic trading was first used by banks in the 80s and 90s. By constantly communicating
proof of the accuracy, explaining the concepts of the algorithm’s decision
making, and exercising corporate responsibility, financial
institutions have successfully normalized funds that are purely run by