Arun Cchawla, CEO, PayMEE FinTECH LTD
I am immensely pleased to introduce myself as an
entrepreneur who is driven by compassion, fuelled by a desire for excellence
and propelled by success, ever since my initiation into the arena of Business.
Starting out as a customer service agent, I practiced an acumen that has been
unique and individualistic. I have had a sterling tenure as VP of a Middle
Eastern Bank. I bring to my ventures a brand value that is crafted and
catapulted to higher echelons through enterprise and endeavor. While I continue
to have an unwavering focus on the professional front, I am just as dedicated
to the realm of self-evolution and growth. I remain an avid reader, have an
affinity for sports, and practice ease of articulation to perfection.
I must take a moment here to say that I am where I
am today with the love and support of my family of four, who have stood by me
through all weather and whose love keeps me motivated.
It has been a great journey so far and I continue
to propel myself to the best extent of my professional acumen to present my
company as the best and of the highest standard, hoping for a better future for
all my prospective employees, partners, and aids in my startup.
PayMEE FinTECH LTD
This company was formed in 2020 at DIFC (Dubai International
Financial Centre), Dubai with an idea to transform my 20 years of experience in
retail debt collection into aims to deconstruct the walls that have surrounded
the traditional financial collection system to collect faster and better but at
a low cost through our bilingual conversational artificial intelligence
SMS/Whatsapp/Email that is diverse and stable. Our innovation helps them by
clustering customers on a COLLECTION RISK SCORE MODEL based on 100 attributes
to understand the behaviour and sentiments predicting default rates in REAL
TIME, which has been extremely short-sighted while using humans.
To elaborate, most financial software today, as
observed, only performs organisational functions, particularly when it comes to
the collection process on consumer lending products by financial institutions
or telecommunication service providers in the Middle East and Asia Pacific
region. Based on past experience, this software has not been capable of
predicting default rates, nor has it been capable of reducing the operating
costs associated with an increase in default rates, which has been managed
manually. Using my domain knowledge in collection, I have innovated a product
that applies machine learning algorithms programming, which helps to improve, and
automate business processes to predict the default rate using linear predictive
modelling to observe customer behaviour – directly resulting from a faster and
more efficient way of communication .
Additionally, it is meaningless unless you act on
it, or if you engage in segmentation activities that are more distracting than
helpful. Hence, in order to help you identify your best current customer
segments, as per my experience, this can be broken down into five clear steps,
from setting up your project to performing customer data analysis, executing
data collection, conducting customer segment analysis and prioritization, and
implementing the results into your organisational strategy.
Bringing Artificial Intelligence and Machine
Learning into life
PayMEE FinTECH LTD has digitalized the collection
process, calling it NEO COLLECTION, wherein we help you not only to collect
faster and better but at a low cost through SMS/WhatsApp/Email. Our innovation
helps you by clustering customers on a risk score model based on 100 attributes
to understand the behaviour and sentiments predicting default rates.
Customer segmentation involves categorizing the
portfolio by industry, location, revenue, account size, and number of employees
to reveal where risk and opportunity live within the portfolio. Those patterns
can then provide key measurable data points for more predictive credit risk
management. These segment clusters serve as the underlying foundation for any
collection action. Some sample segmentation clusters are listed below.
Digital Enablement, Collections Risk: High or Low
Slow Paying Vs. Fast Paying Customer
Disputes: The number of disputed invoices is
another crucial and dynamic component that could prove to be vital in analyzing
the collections' actions based on customer behavior which has been extremely
short-sighted while using humans through our Artificial Intelligence &
Machine Learning model. By segmenting a portfolio into smaller groups with
similar characteristics, one can manage and evaluate the risk tolerances and
develop strategies for reducing, diversifying, and mitigating associated risks.
To segment a portfolio of loans, we apply some combination of the four
aforementioned pillars into a series of steps and decision trees to decide
whether a customer is desirable or undesirable.
What do you mean by consumer risk management, and
how is it related to your product?
CONSUMER RISK MANAGEMENT is part of all retail
lending firms, and risk management encompasses the identification, analysis,
and response to risk factors that form part of the life of a business.
Effective risk management means attempting to control, as much as possible,
future outcomes by acting proactively rather than reactively. Therefore,
effective risk management offers the potential to reduce both the possibility
of a risk occurring and its potential impact.
Literally speaking, it starts with the
identification and evaluation of risk, followed by optimal use of resources to
monitor and minimise the same. Risk generally results from uncertainty.
Our software integrates with clients' data through
API and its automation analyzes the tasks that a client performs in the system.
Learning from common processes, machine learning would generate various
parameters using time series, own defined parameters path and graph analytics
to plot the journey customers are taking and anticipate where they will go. It
performs three primary functions: retrieves data by synchronising across
platforms; combines with in-house data of clients; and runs machine learning
algorithms to make recommendations post slicing them into various parts. This
helps the clients by indicating trends of seasonality, collection issues, and
other events based on the data history that may impact future cash flows.
Vendor management system
A debt collection vendor without a good system to
measure its effectiveness is the best way to waste time and effort. Our vendor
management system makes it easier to manage a debt collection vendor
effectively by paperless automating the process. Stop thinking and take action
as it includes a web portal for clients and vendors to replace human
intervention to a minimum with integrated billing, settlement automation, trust
management, analysis reports and much more.
Rest, contact us for a demonstration.
Methodology and Implementation
Paymee.ai is a fintech startup focused on
addressing the problems associated with the conventional consumer retail debt
collection approach, which is failing everybody involved in it, be it debtors
or creditors. Paymee.ai provides conversational artificial intelligence and
vendor management solutions that help you to get away from the 2-decade old
primordial approach to consumer debt collection, which is extremely
short-sighted while using humans. Paymee.ai platform makes it easy to
understand customer behaviour and sentiments to predict the default rate.
This further maximizes revenue collection, achieves compliance, and at the same
time keeps the costs low.
With the current macro-economic situation, there is
a clamour for a platform from industries in the Middle East and overseas,
including financial services, telecom, and real estate. Our mission is to
assist these industries to help them protect their cash flow and promote the
mental well-being of their customers.
Lenders who are making customers a top priority and
becoming digital-first at every point of the customer journey are witnessing
reductions in NPAs. Interestingly, research done by McKinsey & Company
shows that a digital-first collections strategy has helped leading FIs witness
20-25% reductions in NPAs.
Another interesting insight by McKinsey states that
customers contacted digitally make 12% more payments than those contacted via
traditional channels.
A brave new world
In the coming few months, as customers are more
comfortable than ever using digital channels, it is imperative that lenders
offer a personalised and frictionless digital customer experience. Paymee.ai is
exclusively designed and formulated to cure contemporary businesses of lending
firms having a permanent malaise of defaulted payments. The automated
operational procedures account for a lot of effort and time saving, effective
resource handling, and enable ease of debt collection, vendor management.
Our solution, it combines human intelligence in the
form of artificial intelligence and Machine Learning to offer solutions that
are workable and offer huge relief to both the payer and the payee. This very
effective program can actually work as one's consultant, office assistant and
in some ways on non-working day as well.
Our solution is one stop solution that is process
driven whilst being non messy, easy to follow and convenient as well. As PayMEE
specializes in offering a detailed and analytical report to the user that is
educative and informative, realistic and dynamic. The user of our product is
NBFC ‘s, Banks, Insurance, Real Estate firms, collection vendor and any firm
having a collection unit in their structure.
Post a Comment