MONEDO HOLDING GMBH
DIGITAL REPORT 2020
Monedo: the future of digital lending IN ASSOCIATION WITH
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Monedo: the future of digital lending WRITTEN BY
LEILA HAWKINS PRODUCED BY
GLEN WHITE
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MONEDO
Konstantinos Papakonstantinou, Chief Data Officer at Monedo, explains how data analytics and machine learning are transforming money lending
K
onstantinos Papakonstantinou remembers a time when the use of AI was barely spoken about in the financial sector.
“A few years back it was very hard to go to a conference and hear professionals from different organisations talk about leveraging alternative 04
data sources and machine learning to underwrite customers,” he says. “Now we see many are going down that path, and there’s a real shift towards automation based on machine learning and alternative data.” For online lending provider Monedo, data science, machine learning, artificial and decision intelligence (AI and DI) have been at the heart of the business since it was founded in 2012. Compared to traditional lending banks that verify income based on tax declarations, payslips and information provided by credit bureaus, Monedo acquires data from as many different data sources as possible, which translates into thousands of attributes that describe the customer, and which can then be fed into its machine learning model.
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MONEDO
“ There’s a real shift towards automation based on machine learning and alternative data” — Konstantinos Papakonstantinou, Chief Data Officer, Monedo
The company’s main source of data is Kontomatik, an API that enables financial institutions to verify customers’ identities and access their banking activity. Monedo is also in the process of integrating Kontomatik’s scoring service, which is currently done in-house. Indeed, as the organisation has grown, it has begun to outsource technology to third parties. Dr Papakonstantinou explains that
Dr Papakonstantinou explains: “If you 06
this is a typical process for startups
have raw transaction data, enhanced
as they grow into scale-ups. “Startups
with labels for the different categories
tend to build everything in-house
of transactions, and transformed into
because integrating third parties is just
different features that accurately
too expensive, and because a smaller
describe the financial profile of the user,
team is more agile and has the capac-
you can underwrite more efficiently and
ity and the passion to do this. As you
achieve a much better performance
mature, one of the things you realise
than the banks that underwrite with
is that you need to have valuable part-
traditional methods.”
nerships and strategic alliances; you
Monedo’s data sources CLICK TO WATCH
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2:18
07 should refrain from building everything
worked closely together to develop the
in-house. You really need to leverage
functionality that Monedo needs. “It’s
the existing solutions that are out there.
a balance, I think third party compo-
“It’s a journey we also went through,”
nents certainly find their place in every
he continues. “We had a tendency
organisation that utilises a sophisticated
to build everything in-house, until
architecture for their products,” says
recently we moved to a new cloud-
Dr. Papakonstantinou.
based architecture where we replaced
When he first joined the organi-
all sorts of components with third
sation as a senior data scientist in
party components.”
2014, it was still a relatively young
A key example is Monedo’s loan
company, known as Kreditech. The
management and servicing platform
last two years has seen it undergo
built by Mambu, which provides
a strategy refocus and rebrand as
cloud solutions for banks and lend-
Monedo, with the appointment of
ing businesses. The two companies
Dr Papakonstantinou as Chief Data w w w.mo ne d o. com
Boost financial services with open banking Business around the world transforms their financial services with Kontomatik using data aggregation, online identity verification, fraud prevention and machine learning based risk management.
Learn more
www.kontomatik.com contact@kontomatik.com
Contact us
Officer to lead on data analysis and
solutions and add value for different
apply machine learning to automate
areas of the organisation,” he says.
processes and optimise decisions.
“As early as 6 months after I joined,
“We always wanted to expand the
I was promoted to a management posi-
data science use cases, to build more
tion and formed a multifunction team
E X E C U T I V E P R OF IL E :
Konstantinos Papakonstantinou Title: Chief Data Officer
Company: Monedo Holding GmbH
Industry: Financial Services Location: Germany Konstantinos Papakonstantinou is Monedo’s Chief Data Officer with over 12 years of experience as an individual contributor and a leader. His expertise lies in the field of data science and its applications in operations management, credit risk management, product personalization, conversion optimization, customer acquisition and retention. Prior to joining Kreditech’s data science team in Germany, Konstantinos studied and worked in educational institutes and high-tech organizations in multiple geographies. A passionate advocate of decision intelligence, in his current role in Monedo, Konstantinos is focusing on extracting value from data and unlocking the potential of machine learning in order to automate processes and optimize decisions. Konstantinos holds a MSc in Electrical Engineering from the University of Southern California and a PhD in Statistical Signal Processing from Telecom ParisTech. w w w.mo ne d o. com
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MONEDO
“ We’re constantly looking for new data, but also how to leverage to a greater extent the data we already have in our possession, while at the same time building a framework for data governance” — Konstantinos Papakonstantinou, Chief Data Officer, Monedo
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Monedo’s relationship with Stakeholders CLICK TO WATCH
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2:18
with data scientists, data warehouse engineers and business analysts, in order to start tackling product and marketing analytics.” The company found that during a period of large growth, product and marketing are obvious areas that could benefit from data science, and so Dr Papakonstantinou and his team looked at ways of optimising conversion for its products and increasing the marketing return on investment (ROI). From there the team focused on other areas beyond underwriting, like customer relationship management (CRM) and collections – and then also transformed every process within underwriting, like pricing and affordability, the process of estimating how much customers can afford to repay each month. Having a clear data strategy is crucial for an organisation as data hungry as Monedo. “We’re constantly looking for new data, but also for new ways to leverage, to a greater extent, the data we already have in our possession, while at the same time building a framework for data governance. As you grow from a startup and you w w w.mo ne d o. com
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“ We wanted to expand, to build more solutions and start adding value for different areas of the organisation” — Konstantinos Papakonstantinou, Chief Data Officer, Monedo
and enabling as many stakeholders as possible to use it, is a key to maximise its value.” Monedo is undoubtedly a fastgrowing company. “The more time you spend with a growing organisation like Monedo, the more you also grow with it,” Dr Papakonstantinou says. “Strategies change, priorities change, resources change and you need to
scale up this is an important aspect
be able to adapt to different situations.”
you need to take into account – you
Adaptability has also been important
shouldn’t only care about value crea-
when it comes to managing his team.
tion, but also about value preservation.
“There are very smart, highly quali-
So, having clean good quality data
fied, strongly opinionated managers
Monedo’s View on the change of data & AI CLICK TO WATCH
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1:40
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MONEDO
2012
Year founded
1mn+ Customers worldwide
350+ Number of employees
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in the data space. It’s tantamount to
done on ensuring algorithmic fairness
managing a think tank. Everybody has
and removing any algorithmic bias.
their own opinion because they’re very
Moreover, a lot of publications and
good at what they do, and despite the
discussions at conferences and other
frequent healthy conflicts, you have to
events are looking at the impact of ML
make sure you reach a consensus as
and AI in society. I think that five years
much as possible.”
from now, we probably won’t be talk-
Looking ahead, he sees two main challenges for ML and AI: ethical concerns and challenges with wide-
ing about these challenges as they will have been resolved.” While many people are concerned
spread adoption. However, ultimately,
that AI may lead to a loss of jobs,
he strongly believes these will be
Dr Papakonstantinou believes it will
solved. “A lot of work is already being
actually create new opportunities.
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“We have the analogy of the Industrial
some positions will be lost, a lot more
Revolution. People were initially very
will be created.
sceptical that they would lose their
“More and more people will start
jobs to machines in factories, and
realising that adopting machine learn-
indeed many were replaced, but then
ing and AI in different areas adds value,
people learned how to work with those
and we’ll see that cultural change
machines and more positions were
within more organisations, and across
created than were lost. More positions
more industries.”
will be automated by algorithms but that doesn’t mean we’ll see unemployment by adopting machine learning and AI. We’ll optimise and automate different business processes, so while w w w.mo ne d o. com
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