How Data Analytics Drive Next-Generation Business Financing

How Data Analytics Drive Next-Generation Business Financing

Time Stamp: April 21, 2024 6:00 PM
How Data Analytics Drive Next-Generation Business Financing PlatoBlockchain Data Intelligence. Vertical Search. Ai.

I’ve been in fintech for a number of years, and one thing
has become increasingly clear: the role of data analytics in finance is not
just growing, it’s completely revolutionising how we make lending decisions.
Traditional lending models rely on static data that is often outdated and
generic, but we are now afforded the ability to be increasingly granular when
making financial decisions.

However, while lots of data is great, we need to understand
how to effectively translate this data, action it, and embed it into a better
customer experience. Our revenue-based
finance (RBF) business model relies on a seamless customer journey, so it’s
particularly important to me that we get this right across the business,
especially when handling diverse financial requirements, from smaller loans to
significant investments.

Traditional credit models often view businesses through a
black-and-white lens, primarily relying on credit scores and financial
accounts. In contrast, data analytics offers a more nuanced and informative
approach. We’re now able to look beyond mere numbers, considering factors like
seasonality and recent performance trends. It’s about creating a full picture
of a business’s health and potential, rather than just ticking boxes.

This is particularly important in sectors such as
e-commerce, which is where we initially invested predominantly. When tackling a
concept like seasonality, traditional analysis of balance
sheets or inventory during off-peak seasons can be misleading. Looking at, and
cross-referencing, a range of different data points allows us to
delve deep into the cyclic nature of e-commerce sales and infer correlations
with other inputs such as marketing spend or a specific campaign or event,
identifying peak periods and contextualising performance.

For example, we have financed many e-commerce companies
that typically display low revenue in certain months. However, a detailed
analysis of their historical stock and marketing activities often reveals
significant sales surges during expected key periods, such as Black Friday.

Interestingly, we also observe less predictable spikes. For
example, one of our clients aligns their stock and marketing expenditure with
major global music festivals. They typically experience a notable increase in
revenue about two weeks before these festivals start. This holistic approach
allows us to recognise distinct patterns and tailor our financing to each
business.

Speed, Access, and Flexibility as the Three Pillars of
Modern Financing

Data without action is just that: data. The success of
modern financing, and RBF in particular, can be defined by three key pillars: speed,
access, and flexibility, and data analytics
plays a huge role in this. Data moves at incredible speeds, and it’s the
ability to process and respond to this data in real-time that can elevate a
lender’s product offering.

The advent of cloud computing and open banking has
drastically changed access, allowing vast amounts of data to be processed
almost instantaneously. This real-time access offers unparalleled
flexibility in adjusting offers and funding support based on a company’s
day-to-day performance. AI and machine learning
(read: Large Language Models) will be a pivotal part of business financing in
the future.

The vision will develop tools that can synthesise vast
amounts of data into comprehensible, actionable insights. Imagine being able to
feed financial data into an AI model and receive instant analysis on a
company’s financial health, risks, and opportunities. This is where we are
headed, a future where data analytics not only support but enhance every
aspect of business financing.

I’ve seen first-hand the power of data analytics in
real-time decision-making. We had a repeat customer who hit a rough patch, and
our tools flagged this financial downturn, meaning we could communicate with
them on the fly, adjusting our approach to lending while maintaining full
transparency. This is the kind of agility that data analytics enables, a far
cry from traditional models where assessments could be outdated by months if
not years.

The Problem with Data

Of course, data analysis does come with its own challenges.
One significant hurdle for us is managing data duplication and ensuring its
reliability. In the world of global finance, where we deal with multiple
currencies and languages, data interpretation becomes complex. Take, for
instance, our operations across the UK and Australia.

When we refresh data at midnight in the UK, it’s already
midday in Australia.
This time difference can split a single business day’s data across two days,
complicating our analysis and decision-making process. Then there’s the fact that the sheer volume
of data we handle doesn’t automatically translate to effective decision-making.

Without wanting to sound like a broken record, it’s not just
about collecting vast amounts of data; it’s about converting this data
into an easily interpretable format that informs sound financial decisions.
The information needs to be not only accurate and up-to-date but also presented in a way
that is comprehensible and actionable; there’s a real problem with the
standardisation of data if it is collected from multiple sources.

Without repeating the same point, the focus isn’t solely on gathering extensive data but rather on transforming it into a format that facilitates informed financial choices. Data accuracy and currency are essential, but equally critical is how it’s presented: clear and actionable. The challenge arises when data from various origins lacks standardization.

Open banking is a prime example of this; it’s incredible
that statements and accounts can be presented in so many different formats.
This process of translating raw data into meaningful insight is as crucial as
the data collection itself, and it’s a challenge we continuously strive to
perfect. The future of modern financing looks healthy.

As data points become ever more connected and automated,
there is a huge opportunity for lenders to enhance their decision-making
processes and offer more measured, sustainable, and tailored lending to
customers. The challenge, as outlined above, will be how we make sense of it
all.

I’ve been in fintech for a number of years, and one thing
has become increasingly clear: the role of data analytics in finance is not
just growing, it’s completely revolutionising how we make lending decisions.
Traditional lending models rely on static data that is often outdated and
generic, but we are now afforded the ability to be increasingly granular when
making financial decisions.

However, while lots of data is great, we need to understand
how to effectively translate this data, action it, and embed it into a better
customer experience. Our revenue-based
finance (RBF) business model relies on a seamless customer journey, so it’s
particularly important to me that we get this right across the business,
especially when handling diverse financial requirements, from smaller loans to
significant investments.

Traditional credit models often view businesses through a
black-and-white lens, primarily relying on credit scores and financial
accounts. In contrast, data analytics offers a more nuanced and informative
approach. We’re now able to look beyond mere numbers, considering factors like
seasonality and recent performance trends. It’s about creating a full picture
of a business’s health and potential, rather than just ticking boxes.

This is particularly important in sectors such as
e-commerce, which is where we initially invested predominantly. When tackling a
concept like seasonality, traditional analysis of balance
sheets or inventory during off-peak seasons can be misleading. Looking at, and
cross-referencing, a range of different data points allows us to
delve deep into the cyclic nature of e-commerce sales and infer correlations
with other inputs such as marketing spend or a specific campaign or event,
identifying peak periods and contextualising performance.

For example, we have financed many e-commerce companies
that typically display low revenue in certain months. However, a detailed
analysis of their historical stock and marketing activities often reveals
significant sales surges during expected key periods, such as Black Friday.

Interestingly, we also observe less predictable spikes. For
example, one of our clients aligns their stock and marketing expenditure with
major global music festivals. They typically experience a notable increase in
revenue about two weeks before these festivals start. This holistic approach
allows us to recognise distinct patterns and tailor our financing to each
business.

Speed, Access, and Flexibility as the Three Pillars of
Modern Financing

Data without action is just that: data. The success of
modern financing, and RBF in particular, can be defined by three key pillars: speed,
access, and flexibility, and data analytics
plays a huge role in this. Data moves at incredible speeds, and it’s the
ability to process and respond to this data in real-time that can elevate a
lender’s product offering.

The advent of cloud computing and open banking has
drastically changed access, allowing vast amounts of data to be processed
almost instantaneously. This real-time access offers unparalleled
flexibility in adjusting offers and funding support based on a company’s
day-to-day performance. AI and machine learning
(read: Large Language Models) will be a pivotal part of business financing in
the future.

The vision will develop tools that can synthesise vast
amounts of data into comprehensible, actionable insights. Imagine being able to
feed financial data into an AI model and receive instant analysis on a
company’s financial health, risks, and opportunities. This is where we are
headed, a future where data analytics not only support but enhance every
aspect of business financing.

I’ve seen first-hand the power of data analytics in
real-time decision-making. We had a repeat customer who hit a rough patch, and
our tools flagged this financial downturn, meaning we could communicate with
them on the fly, adjusting our approach to lending while maintaining full
transparency. This is the kind of agility that data analytics enables, a far
cry from traditional models where assessments could be outdated by months if
not years.

The Problem with Data

Of course, data analysis does come with its own challenges.
One significant hurdle for us is managing data duplication and ensuring its
reliability. In the world of global finance, where we deal with multiple
currencies and languages, data interpretation becomes complex. Take, for
instance, our operations across the UK and Australia.

When we refresh data at midnight in the UK, it’s already
midday in Australia.
This time difference can split a single business day’s data across two days,
complicating our analysis and decision-making process. Then there’s the fact that the sheer volume
of data we handle doesn’t automatically translate to effective decision-making.

Without wanting to sound like a broken record, it’s not just
about collecting vast amounts of data; it’s about converting this data
into an easily interpretable format that informs sound financial decisions.
The information needs to be not only accurate and up-to-date but also presented in a way
that is comprehensible and actionable; there’s a real problem with the
standardisation of data if it is collected from multiple sources.

Without repeating the same point, the focus isn’t solely on gathering extensive data but rather on transforming it into a format that facilitates informed financial choices. Data accuracy and currency are essential, but equally critical is how it’s presented: clear and actionable. The challenge arises when data from various origins lacks standardization.

Open banking is a prime example of this; it’s incredible
that statements and accounts can be presented in so many different formats.
This process of translating raw data into meaningful insight is as crucial as
the data collection itself, and it’s a challenge we continuously strive to
perfect. The future of modern financing looks healthy.

As data points become ever more connected and automated,
there is a huge opportunity for lenders to enhance their decision-making
processes and offer more measured, sustainable, and tailored lending to
customers. The challenge, as outlined above, will be how we make sense of it
all.

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