Machine learning as a service enters credit risk

Rosalind Madge our consultant managing the role
Posting date: 1/17/2018 11:24 AM

The UK is one of the most advanced financial services in the world and business is booming. Across industries, machine learning has boosted productivity and service, yet the financial and credit risk services lag behind. Challenger banks and non-traditional payment sources are disrupting the status quo by ushering in a new era of machine learning.

For many industries, machine learning has arrived and it’s leading to big improvements in service and productivity. Though there is a growing trend to revolutionise technology in banking, many financial services organisations struggle to fill posts. It’s time to ask why and brace for financial decision-making with machine learning at its heart.

In the Age of Machines – A New Era of Robotic Relationships

Technology disruptors within the financial services industry face a two-fold balance of keeping to compliance while at the same time creating user-friendly applications. As business trends change, decision makers are faced with new challenges such as cyber security, fraud prevention, and how best to protect their consumers. Meanwhile, working to ensure greater affordability in an industry ripe with new entrants to the marketplace with technology platforms are the fore.

Virtual banking applications challenge traditional banking systems using AI and predictive analytics for a more refined scoring system. Their technology led endeavors help to automated processes and help to speed up their credit decisions for real-time decision making.

Though the benefits are obvious, financial services have been hesitant to move forward until they can be sure no issues will arise. So, to combat their concerns, businesses are redefining robotic relationships. Using emergent and collective problem solving to gain insights into their customer and the services they may require now and in the future.

In this new era, businesses are integrating humans, machines, and data on a large scale creating ‘social machines’. The intuitive balance and sometimes gut reactions of humans coupled with machines probability and risk to determine impact on a number of issues including, but not limited to, economics, financial, credit risk, and psychology. Machines are learning to learn.

How it Works – Machine Learning Fundamentals

At its most basic level, a machine’s function is to analyse data. In machine learning, it combines data analysis and the relationships that exist within it. This allows a business to know the best product or service at the right time to the right audience. Therefore, improving the customer service, satisfaction, and interaction for confident, repeat business.

However, by their nature, Risk Analysts and Credit Risk professionals are cautious and may be leery of allowing machines to make decisions on the behalf of humans. Yet, all the power to make it work is there -- to incorporate algorithms to probabilities to correct and real-time decisions in the most sensitive environments, such as banking, the direction of travel is fairly clear. In fact, a recent Accenture study, suggests 38% of banks plan to invest in machine learning extensively within the next three years. 

Next Steps

Overcoming skepticism is a step to machine learning and realising it already exists in our everyday lives. Already, Amazon and Google have created machine learning systems to improve customer experience. And as machines process and figure out relationships via patterns and trends, services and experiences improve with it. Within credit risk, however, it’s important to include confidence and trust in data quality. 

Maintaining confidence is a significant challenge yet to be overcome due to the vastness of scale when it comes to customer data within the financial services industry. However, methods have evolved to combat this issue such as maintaining web infrastructure.

In fact, with the advent of wireless systems, satellites, and mesh networks, web infrastructure offers greater ability to combat these challenges to build social machines which manage risk through transparency and wide engagement. 

In a market ripe with opportunities, we’re now faced with an ever-increasing skills shortage within Credit Risk. Want to help bridge the gap? Then check out our current vacancies here.

You can also contact our UK Team at 0208 408 6070 or email ukinfo@harnham.com to learn more.

Related blog & news

With over 10 years experience working solely in the Data & Analytics sector our consultants are able to offer detailed insights into the industry.

Visit our Blogs & News portal or check out the related posts below.

Weekly News Digest: 1st - 5th March 2021

This is Harnham’s weekly news digest, the place to come for a quick breakdown of the week’s top news stories from the world of Data & Analytics.  Analytics Insight: Top 10 analytics and business intelligence buzzwords in 2021 If you are a fan of both buzzwords and analytics, then look no further, this article from Analytics Insight is for you! The team at the global publication identify and explore the top buzzwords that are being used to define business intelligence and analytics techniques across the industry in 2021. A few of these include: Predictive AnalyticsEmbedded AnalyticsCognitive ComputingData ScienceX Analytics Ultimately, there are a whole host of buzzwords and key terms being used in Data & Analytics at the moment, but professionals should keep up to date with the core (and most influential) technologies and insights in their area of expertise. Find out more about the top 10 buzzwords and view their definitions here. Forbes: Diversity is what you see, Inclusion is what you do Writing for Forbes, Paolo Gaudiano discusses how to really examine the values and culture of an organisation: you need to change the way in which you think about – or approach – understanding the unique contributions of your team. “Being forced to think about recreating an organization from the inside out, i.e., by actually thinking from the point of view of the individual employees and their experiences, helped us to clarify how we can think about—and define—diversity and inclusion.” It is in considering diversity and inclusion in two separate approaches, that an organisation can truly make this a core area of focus. Afterall, Gaudiano highlights that, “Diversity is a measure of how an individual’s personal characteristics differ from those of the normative majority of an organisation; inclusion is the act of ensuring that people’s experiences within an organisation are not impacted negatively as a result of their personal characteristics.” We need to provide more support, education and tools to ensure that our companies can sustain a growing level of diversity, and to enjoy an inclusive environment. Read more on this here. Computer Weekly: It’s now or never for UK fintech, government told The contributors at Computer Weekly have this week reported on how a Treasury-commissioned review of the UK’s future in financial technology (fintech) has told the government that it must urgently introduce effective policies in five key areas if the fintech industry is to continue to thrive. Policy & Regulation To include creating a new regulatory framework for emerging technology.Skills To include retraining and upskilling adults in support of UK fintech.Investment To include introducing a global family of fintech indices.International To include driving international collaboration and delivering an international action plan for fintech.National Connectivity To include accelerating the development and growth of fintech clusters. The review was comprehensive and provides a startling call to action for senior government officials. Read more on the future growth of the UK’s fintech sector here.  AdAge: First-party data strategies for advertisers and publishers in the age of privacy Can brands partnering with publishers discover better Consumer Insights? That’s the question that this insight from AdAge explores, as brands consider shifting their marketing strategies to align with privacy regulations (and maintain or regain consumer trust). Some of the ways brands are responding are by looking at: First-party Data Strategies Marketers need to pinpoint the right customers to target for different messages. One way to do this is by partnering with publishers and other companies that can offer granular consumer insights built from first-party data in a privacy-safe way.Direct Relationships With Publishers By creating ties with publishers, through trusted networks or direct relationships, brands will be operating in a premium environment, thanks to the data and insights the publishers can provide.Creating A New Digital World Marketing’s priorities now need to shift to first-party data strategies and building trusted networks of first-party data owners. This gives brands an opportunity to not only gain more control over their advertising but also to rebuild consumer trust in advertising. To find out more about how brands and marketers are working together in a digital world, take a look at the article in full here. We've loved seeing all the news from Data & Analytics in the past week, it’s a market full of exciting and dynamic opportunities. To learn more about our work in this space, get in touch with us at info@harnham.com.

The Reliability Of Sleep Trackers For Sleep Data

One in three of us regularly suffer from poor sleep. By this we mean not entering the correct stages of the sleep cycle often enough. During the optimum eight hours of slumber, we should be getting per night, the body should enter three different stages of sleep on a cyclical rotation: light, deep and rapid eye movement (REM). The most important stage of this being deep sleep, of which a healthy adult should be entering for around one to two hours.  Unfortunately, it is often the case, for a vast number of reasons, that many adults struggle to wake up feeling refreshed. From absorbing too much blue light from screens before bed, poor dietary habits or increased levels of stress, there are many factors into why good sleep eludes nearly a third of us daily. Over the past year especially, as a direct result of the pandemic, our sleepless nights have become increasingly worse. It seems anxiety related to COVID-19 has spiked our inability to get good rest. What are the dangers of persistent low-quality sleep? Continual restless nights can have profound effects on both our bodies and our minds. It can place immense stress on the immune system, increasing the risk of becoming seriously ill. Other life-threatening diseases also linked with poor sleep include obesity, heart disease and diabetes.  Our mental state can also be incredibly damaged by consistent poor sleep. Not only does our ability to concentrate reduce, but our susceptibility to mental ill-health, such as depression, increases too.  It is no surprise then that, as a global population, our obsession with the amount of sleep we get per night has skyrocketed in the past few years, consequently seeing the boom of sleep tracking technology. From wearable tech such as the Fitbit and Apple Watches, to other bedside devices and bed sensors, the market for sleep trackers is estimated to reach $62bn in 2021 alone. But is this technology a reliable source of data for our sleep patterns? The problems with sleep trackers Wearable technology can only go so far when it comes to measuring our quality of sleep. Watches especially can usually measure aspects of our body such as heart rate and movement – all of which can be used as indicators of restfulness. However, their consistent accuracy is questionable. According to research, sleep trackers are 78 per cent accurate when it comes to identifying whether we are awake or asleep, which is a pretty good statistic for developing technology, however, this drops dramatically to 38 per cent when estimating how long it takes for users to fall asleep. For true accuracy, sleep should be measured through brainwave activity, eye movement, muscle tension, movement and breathing – all of which can only be looked at through a medical polysomnogram.  Additionally, much like many other sources of technology, sleep trackers have become a troublesome culprit for obsessive behaviour. In 2017, scientists coined the term Orthosomnia, the recognition of a real problem many were, and still are, having with become obsessive, to the point of mental ill-health, around tracking sleep. As stated by neurologist, Guy Leschziner; “If you have a device that is telling you, rightly or wrongly, that your sleep is really bad then that is going to increase your anxiety and may well drive more chronic insomnia." However, sleep trackers aren’t all bad. While not a tool to be used for sleep disorder diagnosis, they can be useful gadgets to help rethink our sleep habits to aim for a better night’s sleep.  The positives of sleep trackers While questions around the accuracy of this technology are prominent, trackers, overall, are pretty good when it comes to recording total sleep time. If used as a guide rather than an aid, sleep trackers can help users get into better sleep habits which in turn will undoubtedly improve their quality of sleep.  If the data is showing that users are only achieving five hours of sleep per night, and they are going to bed very late and rising early, then users may be encouraged to practice better sleep hygiene. From removing any blue light from the bedroom space, to taking an hour before bed to engage in less stimulating activities, such as reading, and practicing methods such as mindfulness or meditation to induce relaxation.  Sleep data from trackers can also be a useful tool to begin conversations with health professionals. Someone who regularly finds themselves groggy in the morning, with the notion that their sleep is badly disturbed, may find solace in sleep tracking data and it may give them the confidence to seek relevant help. While this sort of technology and its data will not be the end point for a diagnosis, it may give both the user and their doctor insight into any potential problems or issues they may be having with sleep.  Ultimately, those using sleep trackers shouldn’t be losing sleep over the data they present. Instead, ensure you are taking the analysis provided with a pinch of salt, and explore this in tandem with how you feel in yourself to assess whether you need to make changes to your sleep routine or seek help for a potential sleep disorder. Data is an incredibly important too, but using this in the right way is absolutely critical. If you're looking for a new role to get you out of bed in the morning or to build up your dream data team, we may be able to help. Take a look at our latest opportunities or get in touch with one of our expert consultants to find out more. 

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