Cognitive cloud lending: Intelligent. Fast. Accurate.
There have
been numerous technology trends over the past several years; some turn out to
be mere hype, while others deliver tangible returns on investment. Identifying
the winners is a priority for all businesses as they evaluate where to focus
spend.
This is
especially true for financial sector businesses such as mortgage lending. Artificial
intelligence (AI), natural language processing (NLP) and machine learning (ML) are
some of the more recent technologies that are solving critical business
challenges and delivering ROI to early adopters. But can the interplay of these
technologies unlock even greater value?
Cloud + AI = Cognitive cloud lending
Marrying AI with
the benefits of the cloud will create cognitive cloud platforms that can have
far-reaching impact for mortgage companies. Cognitive cloud lending platforms
and solutions can provide lenders, borrowers, brokers, dealers, real estate
agents with more self-service and transparency options throughout the lending
experience. Further, they can extend this ecosystem, making it possible to
create and bring cognitive computing applications to the masses.
An apt
example here would be the Mortgage Cadence Platform. It is the only
comprehensive single system of record technology which is extensible through an
open API layer, allowing banks/lenders to connect with third-party providers of
their choice. It also offers valuation, verification, title, credit, fee, and
compliance services. Another example would be Cloudvirga, the firm behind the
cloud-based intelligent Mortgage Platform® (iMP). Designed to streamline the
mortgage process, this platform is digitalizing the mortgage industry by
deploying automated workflows to reduce cost, increase transparency and shorten
the time it takes to close a loan for both borrowers and lenders.
Four ways to enable cognitive cloud lending
1. Replace monolithic software solutions - To get projects up and running, most large companies use monolithic
IT architecture as it is faster to set up. However, as the system grows, the
code and architecture become more complex and more developers are needed to
maintain these. Companies also lose critical speed, flexibility and agility to respond
to the market. Organizations embarking on digital transformation should
consider an agile style of application architecture that enables rapid delivery
of new cloud-based digital services.
Microservices
architecture is one way of achieving this. This architecture encapsulates
business entities that orchestrate multiple business entities as in the case of
credit evaluation. For example, microservices can support and automate an
existing loan origination system. It can accelerate risk decisioning processes
in a secure manner whereby risk scores are calculated within a second of the
application being received. The result? Applications can be approved in a
fraction of the average 'time to cash'. To put this in context, the average time
to cash is 40 days and even top performers take 18 days.
2. Encapsulate the core with process APIs - Instead of designing infrastructure around applications,
service-oriented architecture (SOA) focuses on specific services. SOA is very
useful in supporting business processes without having to worry about the
underlying applications. Process APIs act as the communication layer, combining
data from disparate sources and making it easily discoverable. For mortgage
lenders, using process APIs brings about visibility, programmable services and greater
agility.
Faced with
numerous requests from customers looking to switch mortgages, a US-based home
mortgage lender decided to automate its loan underwriting system. By leveraging
SOA, the mortgage company can employ reporting tools and follow industry best
practices, all while managing loan applications seamlessly.
3. Leverage cognitive BPM for intelligent automated services - Cognitive business process management (BPM) refers to the new BPM
paradigm enabled by cognitive computing. It encompasses all the typical BPM
aspects in addition to three transformational aspects: Firstly, CBPM will drive
knowledge acquisition at scale, enabling knowledge-intensive processes (KiPs). Secondly,
a 'plan-act-learn' cycle will emerge as the new process meta-model. Thirdly,
the models can study process descriptions both during design and run-time. This
will open up opportunities for new levels of automation and business process
support for both traditional business processes and KiPs. A research paper on
CBPM hypothesizes that cognitive computing will leverage AI to manage dynamic processes,
achieving intelligent automation, continuous improvement and higher performance.
In the
mortgage industry, cognitive BPM can be useful to determine customer
satisfaction. Let's take an example, when a customer's loan gets approved,
he/she is directed to the bank's loan servicing department. This department monitors
changes to the payment plan and ensures proper payment collection. Its
operations include outbound and inbound calls that generate call transcripts. Now,
if the bank/lender applies cognitive analysis to this process, it can determine
whether its employees are asking the right questions, how efficiently are they
working, how polite are they with the customers, so on and so forth. Based on
these insights, the bank can take actions to either improve customer
communication or leverage new ways to improve customer satisfaction as well as the
bank's profitability.
4. Connect front and back-end systems - To improve lending and advisory capabilities in future, banks will
need to harness low-code applications and automation technologies that foster connections
between front-end and back-end systems. This will also support smart case
management, a capability that allows adaptive business processes In a post COVID-19 scenario, this kind of agility can mean faster loan
disbursals for SMEs seeking financial stimulus from their respective
governments. In the UK, nearly 69,000 loans were approved within 24 hours of a
100% government-backed scheme, highlighting the mounting pressure on loan
processing teams. Using APIs to facilitate back-end
integration promotes reusability as it unlocks new opportunities to integrate
with other front-end technologies like chatbots.
Intellect
Design Arena, a financial technology provider, built the Intellect Digital
Lending (IDL) 20 on an 'always on, always aware' concept. Designed on a
do-it-yourself (DIY) principle, IDL 20 allows banks to create their products anywhere,
anytime. Banks can also make real-time informed credit decisions. It also
enables banks to deliver a real-time solution to their customers by giving them
access to a 360-degree view of their credit portfolio. Its fully automated
robust architecture reduces operational costs by increasing efficiency.
Conclusion
The mortgage
lending business needs to leverage new trends driven by technologies like
cognitive cloud that can accelerate lending processes, making them intuitive, smart
and relevant to customers. It is up to banks to align these technologies with
the business and develop innovative lending models that leverage the power of
intelligence.