Finance: How to Make the Most of Machine Learning

Rob Prevett, founder of Disruption Summit Europe

The use of machine learning in finance can clearly do wonders, it’s a technology that is such a great fit for the financial services industry.

So let’s take a closer look at how companies can utilise it.

As a subset of data science, machine learning uses specific algorithms and chosen datasets to train mathematical models to find patterns, make predictions, segmentation, and more.

Plus, you can regularly update the mathematical models, so they can effectively learn from both experience and new data.

So there is a clear fit with the quantitative nature of the financial services industry. Yet what factors are inhibiting its uptake in the industry to date?

The key reasons for this are because it’s costly; because the incumbents in the industry are slow to update their tech infrastructure, because they don’t quite understand the long-term value of machine learning and, finally, because there is a skill shortage of data scientists and machine learning engineers.

 Efficiency via Robotic Process Automation (RPA)

RPA is currently one of the most common applications of ML in finance, replacing manual work, automate repetitive tasks, and increasing productivity and accuracy.

So machine learning helps to automate tasks in finance: using things such as chatbots, call-centre automation, legal work automation, gamification of employee trainings, and other innovations.

Improved financial security

Cybersecurity is a major threat to the finance industry. Machine learning is able to make sense of a large amount of data and compare each transaction against an account history, thus enhancing fraud detection, cybersecurity, and financial monitoring.

This means that an ML algorithm learns from each action an account user takes and can make an accurate prediction about what is and isn’t characteristic of their behaviour.

Underwriting and algorithmic trading

Underwriting is one of the most suitable tasks for machine learning algorithms, which can be trained on big datasets from real consumer accounts of banks and insurance companies.

Algorithmic trading is an extension of current machine learning algorithms, which usually don’t trade themselves but rather help humans make better trading decisions – with models detecting unusual conditions and thus triggering the system to stop trading and so on.

The barriers to using machine learning in finance

While developing machine learning solutions, financial companies often struggle to understand their real business KPIs.

Plus, financial services companies often have fragmented bits of data stored at numerous locations – including CRMs, reporting software, and more – data which takes a considerable amount of time extracting, transforming and loading (ETL) and cleaning. So there is an organisational creep at work which de-prioritises machine learning initiatives.

Big Data Engineering &Off-The-Peg ML solutions

In many cases, the financial services company in question might well need to focus on getting its data in order, via big data engineering tech, before it is in a position to properly implement machine learning.

Even then, if you’re in a position to adopt machine learning, it’s also often far more cost-effective to look at what’s on offer from tech companies like Google, Microsoft, and IBM – all of whom create machine learning software-as-a-service that can solve numerous specific business tasks.

However, you might be in a position where you don’t want to give up control and want to develop your own AI and machine learning solution from scratch. In which case you need to really have to make sure you have viable KPIs and realistic estimates; understand that ML developers need to make an investigation, which takes up additional time and costs and that, to validate an idea, you need to have the data collected. Otherwise, you would have to involve a data engineer to collect the data first.

Essentially, you need to decide what your KPIs are and what you want to achieve from using machine learning within your organisation. Then you need to look at your current data storage and data warehousing and consider if it is in a fit shape yet to benefit from machine learning.

And finally, if you are in a position to adopt machine learning within your organisation, you need to find the right third-party partners or skilled staff in-house to help you implement it.

Or of course, you could just buy your own machine learning start-up. But I hear that they are quite costly, these days…

How else could the financial services industry benefit from machine learning innovations? To learn more about machine learning and its impact on finance you can head over to this year’s Disruption Summit Europe – see more at https://disruption-summit.com/

Rob Prevett, founder of Disruption Summit Europe

The use of machine learning in finance can clearly do wonders, it’s a technology that is such a great fit for the financial services industry.

So let’s take a closer look at how companies can utilise it.

As a subset of data science, machine learning uses specific algorithms and chosen datasets to train mathematical models to find patterns, make predictions, segmentation, and more.

Plus, you can regularly update the mathematical models, so they can effectively learn from both experience and new data.

So there is a clear fit with the quantitative nature of the financial services industry. Yet what factors are inhibiting its uptake in the industry to date?

The key reasons for this are because it’s costly; because the incumbents in the industry are slow to update their tech infrastructure, because they don’t quite understand the long-term value of machine learning and, finally, because there is a skill shortage of data scientists and machine learning engineers.

 Efficiency via Robotic Process Automation (RPA)

RPA is currently one of the most common applications of ML in finance, replacing manual work, automate repetitive tasks, and increasing productivity and accuracy.

So machine learning helps to automate tasks in finance: using things such as chatbots, call-centre automation, legal work automation, gamification of employee trainings, and other innovations.

Improved financial security

Cybersecurity is a major threat to the finance industry. Machine learning is able to make sense of a large amount of data and compare each transaction against an account history, thus enhancing fraud detection, cybersecurity, and financial monitoring.

This means that an ML algorithm learns from each action an account user takes and can make an accurate prediction about what is and isn’t characteristic of their behaviour.

Underwriting and algorithmic trading

Underwriting is one of the most suitable tasks for machine learning algorithms, which can be trained on big datasets from real consumer accounts of banks and insurance companies.

Algorithmic trading is an extension of current machine learning algorithms, which usually don’t trade themselves but rather help humans make better trading decisions – with models detecting unusual conditions and thus triggering the system to stop trading and so on.

The barriers to using machine learning in finance

While developing machine learning solutions, financial companies often struggle to understand their real business KPIs.

Plus, financial services companies often have fragmented bits of data stored at numerous locations – including CRMs, reporting software, and more – data which takes a considerable amount of time extracting, transforming and loading (ETL) and cleaning. So there is an organisational creep at work which de-prioritises machine learning initiatives.

Big Data Engineering &Off-The-Peg ML solutions

In many cases, the financial services company in question might well need to focus on getting its data in order, via big data engineering tech, before it is in a position to properly implement machine learning.

Even then, if you’re in a position to adopt machine learning, it’s also often far more cost-effective to look at what’s on offer from tech companies like Google, Microsoft, and IBM – all of whom create machine learning software-as-a-service that can solve numerous specific business tasks.

However, you might be in a position where you don’t want to give up control and want to develop your own AI and machine learning solution from scratch. In which case you need to really have to make sure you have viable KPIs and realistic estimates; understand that ML developers need to make an investigation, which takes up additional time and costs and that, to validate an idea, you need to have the data collected. Otherwise, you would have to involve a data engineer to collect the data first.

Essentially, you need to decide what your KPIs are and what you want to achieve from using machine learning within your organisation. Then you need to look at your current data storage and data warehousing and consider if it is in a fit shape yet to benefit from machine learning.

And finally, if you are in a position to adopt machine learning within your organisation, you need to find the right third-party partners or skilled staff in-house to help you implement it.

Or of course, you could just buy your own machine learning start-up. But I hear that they are quite costly, these days…

How else could the financial services industry benefit from machine learning innovations? To learn more about machine learning and its impact on finance you can head over to this year’s Disruption Summit Europe – see more at https://disruption-summit.com/