360Quadrants,
the world’s only comparison platform that combines expert analysis with
crowdsourced reviews, has released a quadrant on AI in Fintech solutions to help businesses make quicker and more
informed decisions.
Artificial Intelligence (AI) in Fintech refers to the theory and
development of computer systems capable of performing finance-related tasks
that usually require human intelligence. It is an application of AI technology
used in the financial sector to design investment strategies, detect anomalies
with pattern & voice recognition, and conduct market analysis with data
mining. The AI in Fintech solutions market for this quadrant is defined as the
summation of AI-enabled Fintech solutions and services.
For this quadrant, the components of AI in Fintech are mainly segmented
into solutions and software. Major application areas of AI in Fintech solutions
are virtual assistants, business analytics & reporting, and customer
behavioral analytics. These solutions have been analyzed based on cloud and
on-premise deployment.
ZestFinance has been identified as
an emerging company
in the AI in Fintech Solutions space given its extensive product features,
high product quality, reliability, suitable channel strategy, and wide
geographical footprint. 360Quadrants also lists competitors
of ZestFinance in the AI in Fintech space.
AI
in Fintech offerings from ZestFinance aim
to deliver valuable insights and intelligence from data. The company has an
established product portfolio with a robust market presence and business
strategy. Some
of the major developments of the company include:
ZestFinance collaborates with Microsoft Cloud towards delivery
of first fully explainable AI solution for credit underwriting
ZestFinance, in collaboration with
Microsoft, is pioneering a fully explainable AI solution that has applications
across highly regulated industries, with the financial sector being on top of
the list. Zest aims to achieve this solution through direct deployment of its machine
learning (ML) software tools on Microsoft Azure and Machine Learning Server
platforms.
Financial institutions, using Azure and
Machine Learning Server, will be able to apply Zest's ZAML suite of tools for building,
deploying, and monitoring explainable ML credit models. This partnership brings
together Zest's deep commitment to explainable ML and the potential of
Microsoft's vast technology. Through the use of more data and better math, ML
credit models can locate worthy borrowers that are overlooked by legacy models.
By safely acquiring new customers and re-calculating risk throughout the credit
spectrum, Zest customers not only see an increase in approval rates (15%) but
also witness a decline in credit losses (30%). As more efficient models run in
the cloud, financial institutions can provide more credit overall at a decreased
cost.
Subprime auto lending industry: ZestFinance partners with Prestige
Financial Services for deployment of first AI-Powered credit scoring model
ZestFinance has teamed with subprime auto
lender Prestige Financial Services for the implementation of the industry's
first ‘fully explainable’ ML model for the prediction of borrower risk.
Prestige will stop using legacy scoring techniques and deploy Zest Automated
Machine Learning (ZAML) software instead, enabling the use of several thousand
data variables as well as advanced math for development of its credit models.
For Prestige, these new models allow the
achievement of a 36% boost in new applicants, and subsequently, a 14% increase
in approval rate. Within six months of the AI-based credit underwriting model being
deployed, Prestige has seen a 100% increase in its lending volume without taking
on additional portfolio risk.
When Prestige engaged ZestFinance, it had
raised its thresholds for underwriting to such an extent that approximately 7
out of 10 applicants were denied loans. Through the application of machine
learning, Prestige was able to boost efficiency when rank-ordering risk across
all borrower types, thus allowing them to decline loans to risky borrowers while
replacing them with thin-file or new-to-credit clients who had higher creditworthiness
than traditional credit scoring models suggested.
