Published by Gbaf News
Posted on January 3, 2018
8 min readLast updated: January 21, 2026
Mark Davison, Chief Data Officer, Callcredit Information Group
Whether it is the retail industry’s ability to make recommendations based on a consumer’s online behaviour, or healthcare professionals using computer-assisted diagnoses to determine diseases quicker than ever before, machine learning (ML) has become a game changer in a wide variety of sectors.
However, despite the opportunities it presents for fraud prevention and affordability modelling, and the wealth of data available to model from,ML adoption rates have not been as widespread as in other industries. The Digital Banking Report revealed that only 37% of financial services organisations are expecting to use artificial intelligence (AI) functionality within the next 18 months. Owing in part to the sector’s risk-averse nature, many have been reluctant to embrace the potential positives of the technology, instead ffavouring to stick with tried and tested techniques. But the industry cannot afford to stand still, and 2018 should be the year that financial services (FS) businesses dispel some of the prevalent myths standing in the way of them fully harnessingML’s potential.
Myth 1 – Machine learning is too risky for financial services
Doubts over new technology are understandable in FS.Trust has an entirely different and elevated meaning in the sector compared to, for example, retail, in which a trial mentality is more acceptable. As a result, fraud prevention specialists may place more trust in traditional systems to flag a fraudulent application.
However, as fraudsters grow ever more sophisticated with their techniques, a greater array of data and tools are required to detect suspicious or fraudulent activity. Through ML, computers are able to pull together a vast amount of contextual data to determine whether an application is legitimate. Over time, automated systems can become more adept at spotting irregular data, resulting in more efficient fraud detection. Thinkingthat ML is too much of a risk in FS is risky in itself, as there is so much at stake when it comes to fighting fraud. By letting ML make decisions based on accurate and robust data, fraud professionals can spend more time and attention on what the parameters of these decisions should be.
Myth 2 – Machine learning is not ready to make better decisions
Financial institutions are understandably hesitant to adopt unproven technology, but whether it’s Amazon’s product recommendations or Siri, we already trust ML implicitly – often without even realising.The benefits of this realisation are not limited to fighting fraud, asML can also be a great asset in credit risk and affordability modelling.
Through analysing huge amounts of existing data, the technology can help lenders support their decisions to both consumers and regulators. It can also be used to flag applications where a customer is likely to default, as sample sets of applications can be contrasted with actual decisions of the time to modify a business’s risk profile. Far from exploring uncharted territory, ML focuses on using data to allow both man and machine to make better decisions together.
Myth 3 – Machine learning is difficult and time-consuming to implement
Owing to its growing status as the next generation of essential predictive tools, it is justifiable to view ML as an arcane technology that only select specialists can understand. But, in reality it is relatively simple to adopt, and is primarily focused on supplementing traditional methods by working alongside existing systems.
As its name may suggest, ML is a largely autonomous process, and develops its algorithms over time as more and more data is used.This results in the quick and easy creation of more bespoke, dynamic and constantly developing models. Far from being a time-consuming burden, the way ML can assist and supplement existing jobs will lead to dramatic increases in productivity and efficiency.
Why machine learning is a new dawn for financial services
2018 is set to be a landmark year for ML’s adoption in FS, as the tools required to take advantage of it are now widely available. As a result, the most important step to overcome scepticism amongst the industry is understanding that, despite the myths, society does actually already trust machine learning implicitly. Firms that are able to accept this fact and embrace the technology won’t just have the potential for incredible increases in productivity and efficiency, but can ensure they remain relevant in the future.
Mark Davison, Chief Data Officer, Callcredit Information Group
Whether it is the retail industry’s ability to make recommendations based on a consumer’s online behaviour, or healthcare professionals using computer-assisted diagnoses to determine diseases quicker than ever before, machine learning (ML) has become a game changer in a wide variety of sectors.
However, despite the opportunities it presents for fraud prevention and affordability modelling, and the wealth of data available to model from,ML adoption rates have not been as widespread as in other industries. The Digital Banking Report revealed that only 37% of financial services organisations are expecting to use artificial intelligence (AI) functionality within the next 18 months. Owing in part to the sector’s risk-averse nature, many have been reluctant to embrace the potential positives of the technology, instead ffavouring to stick with tried and tested techniques. But the industry cannot afford to stand still, and 2018 should be the year that financial services (FS) businesses dispel some of the prevalent myths standing in the way of them fully harnessingML’s potential.
Myth 1 – Machine learning is too risky for financial services
Doubts over new technology are understandable in FS.Trust has an entirely different and elevated meaning in the sector compared to, for example, retail, in which a trial mentality is more acceptable. As a result, fraud prevention specialists may place more trust in traditional systems to flag a fraudulent application.
However, as fraudsters grow ever more sophisticated with their techniques, a greater array of data and tools are required to detect suspicious or fraudulent activity. Through ML, computers are able to pull together a vast amount of contextual data to determine whether an application is legitimate. Over time, automated systems can become more adept at spotting irregular data, resulting in more efficient fraud detection. Thinkingthat ML is too much of a risk in FS is risky in itself, as there is so much at stake when it comes to fighting fraud. By letting ML make decisions based on accurate and robust data, fraud professionals can spend more time and attention on what the parameters of these decisions should be.
Myth 2 – Machine learning is not ready to make better decisions
Financial institutions are understandably hesitant to adopt unproven technology, but whether it’s Amazon’s product recommendations or Siri, we already trust ML implicitly – often without even realising.The benefits of this realisation are not limited to fighting fraud, asML can also be a great asset in credit risk and affordability modelling.
Through analysing huge amounts of existing data, the technology can help lenders support their decisions to both consumers and regulators. It can also be used to flag applications where a customer is likely to default, as sample sets of applications can be contrasted with actual decisions of the time to modify a business’s risk profile. Far from exploring uncharted territory, ML focuses on using data to allow both man and machine to make better decisions together.
Myth 3 – Machine learning is difficult and time-consuming to implement
Owing to its growing status as the next generation of essential predictive tools, it is justifiable to view ML as an arcane technology that only select specialists can understand. But, in reality it is relatively simple to adopt, and is primarily focused on supplementing traditional methods by working alongside existing systems.
As its name may suggest, ML is a largely autonomous process, and develops its algorithms over time as more and more data is used.This results in the quick and easy creation of more bespoke, dynamic and constantly developing models. Far from being a time-consuming burden, the way ML can assist and supplement existing jobs will lead to dramatic increases in productivity and efficiency.
Why machine learning is a new dawn for financial services
2018 is set to be a landmark year for ML’s adoption in FS, as the tools required to take advantage of it are now widely available. As a result, the most important step to overcome scepticism amongst the industry is understanding that, despite the myths, society does actually already trust machine learning implicitly. Firms that are able to accept this fact and embrace the technology won’t just have the potential for incredible increases in productivity and efficiency, but can ensure they remain relevant in the future.
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