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Why Should the Financial Services Industry Embrace Machine Learning?

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Why Should the Financial Services Industry Embrace Machine Learning?

Steve Wilcockson, Financial Services Industry Lead, MathWorks

The use of machine learning in model governance is in its infancy, understandably so. While the technology presents challenges and frequently raises flags, long term advantages do and will outweigh short term obstacles.

Models run the world, and it is right, given the shocks of 2007 and 2008, that financial ones be managed and governed well. In the decade since, initiatives have been put in place to promote good model development and use, such as the ECB’s TRIM and the UK’s Prudential Regulation Authority’s recent focus on stress test model management. However, while there has been progress, financial services, including in risk management, lag other industries in ‘risk-aware’model governance. Current timescales of years are simply too long for banks and other systemically important financial institutions to submit new or changing models, occur with relative inconsistency across model proposers and supervisor reviewers, and with insufficient attention to good process compared with those in other industries.

Compared with firms working in ‘high integrity’ stringently-regulated industries, e.g. aerospace, medical, robotics, automotive,financial services regulated firms are less agile in their development, test, audit and submission processes, in part because of throw-over-the-wall processes but also their longstanding desire to play with the latest and greatest tools and technologies, adding risk and complexity. Their resource and skill-challenged regulators struggle to keep pace with those same latest and greatest technologies.

On the haphazard use of bleeding/leading-edge technologies, the industry faces paradoxes of on one hand a need to be seen to be moving with the times embracing the latest and greatest, but on the other facing continuing challenges of maintaining legacy “hip” technologies and applying them across technical and business silos. Risk models, for example, need to be assessed by lawyers, accountants, quants, data scientists and IT. In addition, courtesy of the SM & CR-likedirectives, executives with reputations stung by decades of misconduct must also show willing in understanding risk model processes.

The paradox of machine learning in risk management

Steve Wilcockson

Steve Wilcockson

At the heart of current digitisation fashions, there is a culture clash between traditional risk management and machine learning. In making the case for machine learning applicability to risk, one bullish CRO suggests machine learning can improve risk model accuracy by 25-30%, including in credit models. Other senior risk managers have purported that credit risk model shelf lives of 3 to 5 years can increase to 5 to 8 years, primarily due to model “adaptability” of machine learning.On the other hand, criticisms stem from methods reputedly being non-transparent, “black box” and un-reproducible, thus problematic in validation, audit and regulatoryscrutiny workflows. Such criticisms while true in part are over-simplistic, verging onmyth. One machine learning approach for example, so-called classification trees, have an easily explainable observable model structure.

An example

To demonstrate how machine learning models can improve accuracy while tackling the perceived challenge of replication, we present a nonlinear credit scoring application comparing a traditional scorecard model to a neural network and a classification tree. This task here was to predict default (a 0 or 1 value), and model as a classification problem. When looking at the receiver operating characteristic (ROC) curve we see the traditional scorecard model performs well when compared with a shallow neural network and a standard logistic regression. The best performing model by a healthy margin was a classification tree. This type of improvement in predictive capability is why machine learning attracts attention. Of the models explored, the neural network is the hardest to explain to regulators since the learned features (the parameters) are not easy to map to explanatory features from the data. However, unlike a neural network, the successful classification tree can be viewed and explained. For example, you can zoom in to show the logic for the different branches for a scenario where an employed homeowner would be predicted to default (1 on the leaf node). Classification trees, with a known structure, lend themselves naturally to explanations. They are easily explored for sensitivity to changes in parameters by directly examining specific regions of the tree.  Thus, a classification tree’s dominant features and parameter sensitivities can be readily incorporated into risk reports for model reviewers to understand and judge the validity of the model results.

Figure 1. Multiple approaches to modelling credit card default data (bottom right). ROC curves (top right) measure the performance of a model. A perfect model would immediately step up to 1 and remain there as you move along the x-axis. The best model is a classification tree (top left); tree models can be viewed (bottom left) for understanding the logic behind the trees construction.

Figure 1. Multiple approaches to modelling credit card default data (bottom right). ROC curves (top right) measure the performance of a model. A perfect model would immediately step up to 1 and remain there as you move along the x-axis. The best model is a classification tree (top left); tree models can be viewed (bottom left) for understanding the logic behind the trees construction.

Augmenting traditional processes with machine learning

One related challenge organisations face is model bias. Suppose a recently-hired credit risk analyst studied their PhD in neural networks, techniques others on the credit risk team have little experience of. They may be disposed to defer to the expertise of the new hire, who may promote a neural network classification process as the latest, best and greatest. This hypothetical situation represents model bias and thereby risk, since the decision to use neural networks was a subjective decision of an individual with limited perspective and approved by an unengaged team. Now, this is a fictitiousexample, but you get the point. Human model bias can elevate imperfect model and feature selection, therefore a risk. Regulated firms must mitigate against it and supervisors be concerned by it.

Machine learningcan infuse objectivity into model and data governance. Model selection involves selecting a statistical model from a set of candidate models. Sitting in the machine learning selection stable are Lasso regularisation methods, which can encompass generalised linear models, least squares, and proportional hazards models, all common methods in credit risk.

Organisations could also apply ahybrid modelling approach, in which machine learning is used to help drive sound modelling feature identification for traditional scorecard models, offering better and still explainable models. In addition, the neural network approach of our enthusiastic new hire may be useful to“challenge” the dominant methodologies, and it may be appropriate for a bankModel Validation Unit to pose it as an insightful alternative. As a side effect, this activity could help the bank learn about neural networks, whether for credit risk or other tasks, without it necessarily being the mainstay approach.

New risk categories 

Machine learning helps banks and other financial institutions deal with new risks such as fraud, money laundering and misconduct detection. The technology is an invaluable part of preventing banks from unknowingly servicing criminals as part of know-your-customer strategies, or also in voice recognition technologies to determine conduct issues in Skype/phone selling. Where theproblem is “new”, draws on alternative data (voice, geolocation, text, sentiment, social media) and big data, machine learning and deep learning naturally forms part of the toolkit. Try opening a bank account in a foreign country, and watch those algorithms scour your submitted details, trying to work out if you really are who you say you are and if your profile might match those of irreputable others.

 It’s not all about machine learning 

Let’s be clear. While this article has focussed on machine learning, it is simply one tool in the toolbox, albeit a highly fashionable one. Lasso methods, while useful, are not the be all and end all for model selection. The key thing for a regulated institution is that they have the flexibility to build, adapt and monitor multiple models consistently, identify key parameters and features with intelligence and transparency, apply appropriate models from the library, and capture comprehensive output. Ideally, the output should be a “package”, model outcomes with accompanying data and model references, including automatically compiled details of who (bot or human) did what, why, how and when. That pack can be augmented at each lifecycle stage, through validation and audit, passed onto the regulator as a cohesive entity, ideally with replication capabilities. Machine learning is not in isolation the answer, but it can help. The good news though is that the tooling that banks – and supervisors – can use to drive the model lifecycle can also invokemachine learning, help negate perceived non-transparency and replicability in some cases and in all cases a means to challenge and validate.

The 10-year anniversary of the global financial crisis offers us a stark reminder of the importance of model governance. The abuse and misuse of models within siloed, technologically complex andoverly-competitive cultures caused the crisis, but models also provided solutions. Machine learning, in the wrong hands, could be the Gaussian copula of the next crisis and we should absolutely consider the technology’s model risk. However, it can also help mitigate risk and improve model process, and it’s the model process we must continue to lavish attention on, and thus come into line with other industries.

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Top 5 Ways To Lose Your Video Files

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Most Video Content Created in the Summer Months, Finds Veritas Research

There are lots of reasons why you can lose video files in your system or device. While some of these problems are avoidable, others are inevitable. Simply put, it is only a matter of time before the latter problems will be experienced. The major challenge is that most people don’t understand why their video files are either missing, damaged or deleted.

Are you amongst those mentioned above? Do you always ask why a video file can get lost, deleted or damaged? Don’t waste time to rack your brain on a such topic/matter. This is because you have come to the right place. This well-researched blog will provide you all the top reasons why you are losing your video files. Before thinking about which video recovery software to start using, it is recommended you know why files are getting lost in your system.

Power outage

This is a common problem that is responsible for loss of data. However, most people are yet to discover it. Have you ever noticed any situation whereby there is sudden power outage in your system? Simply put, your system suddenly shuts down. In such case, applications and other files (photos, videos, audios and doc) will be closed without any prior warning. The implication of this is that your video files are likely to get damaged.

Top 5 Ways To Lose Your Video Files 1

You may want to argue that systems come with batteries. Therefore, there will not be a problem of power shut down. This is not true in any way as power shut down can happen due to some reasons. It could even be that your system is malfunctioning. Even when your video files are not damaged through such incident, there is every chance that your hard drive will be adversely affected.

Faulty hard drive

If your hard drive is damaged due to one reason or another, there is every chance that your video files will be lost, damaged or deleted. Most of the cases related to data losses can be traced to malfunctioning hard drive. In case you don’t know, hard drives are very fragile. They can easily become faulty because of poor handling. If your hard drive crashes, it is recommended you find out what must have led to such problem. Trying to use a data recovery tool without knowing the root cause of the problem won’t do any good.

There are numerous factors responsible for a faulty hard drive. These could be hot system, frequent crashing, constant freezing, slow processing speed, booting up issues. The longer you ignore these problems; that is how your hard drive will be further damaged. This will in turn lead to loss of video files.

Human error

The truth is that as humans, we are always bound to make mistakes. These could sometimes prove very costly since they can bring about loss of video files. There are cases when important business files have been deleted mistakenly in the past. Human errors are very common. They can bring about formatting of hard drive, data loss and repair of system.

The only way to avoid this problem is to ensure you understand files to be deleted or retained. Most people don’t read messages displayed in dialogue boxes before clicking on “Delete” buttons. This is an easy way to delete important video files without knowing.

Computer viruses

Computer virus is one of the major reasons why people lose video files. Just as the digital world is becoming increasingly popular, viruses and malware are also waxing stronger. One of the most notable causes is when you begin to explore unsecured websites. It can also happen when corrupt files are downloaded and installed in your system.

When your system has been compromised by viruses and malware, there is a very high chance of losing vital files like doc, videos, audios and photos. You will try to open these files but they won’t respond. The best way to overcome this problem is using anti-virus software. This will help to monitor your browsing habit online.

Hard drive formatting

This happens to be another major cause of data loss. Most people format their hard drive without backing up vital files. In the end, these files are lost. Without the help of a paid or free recovery software, you may struggle to get back such data.

It is recommended that you backup important video files before initiating such a process. There are lots of ways to do this. For instance, you can use an external hard drive or even store such files online. This will save you the stress of spending unnecessarily on a data recovery software.

Final words

When it comes to losing your video files, there are lots of reasons which could be responsible for such problem. The tips shared above can help you to a great extent. All you have to do is ensure they are avoided.

 

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BNP Paribas joins forces with Orange Business Services to deploy SD-WAN for 1,800 retail sites in France

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  • Co-construction approach ensures business continuity during deployment

BNP Paribas has chosen Orange Business Services to deploy an SD-WAN solution in more than 1,800 bank branches across France. Focused on developing and integrating new digital solutions, BNP Paribas continues to provide the highest standards to improve user experience for customers and employees alike.

By integrating Flexible SD-WAN from Orange Business Services, BNP Paribas benefits from a modern and agile technological platform to accelerate its digital transformation. This enables quick and easy deployment of multiple services, such as dynamic routing and path selection, with scalability and flexibility. It also empowers administrators to monitor infrastructure performance and resolve potential network congestion through simple software modifications, thereby optimizing application performance. By deploying SD-WAN, BNP Paribas can take advantage of a fully secure hybrid network that is natively multi-cloud, multi-access and multi-application. The Bank will also benefit from optimized and centralized management and intelligent routing for its new infrastructure.

Close collaboration between business and IT for greater agility

From the start of the project, experts from Orange Business Services and BNP Paribas built the solution design together and prioritized the features to be deployed. More than 3,600 access lines—two per branch, including one Internet access line – are currently being rolled out with a focus on maintaining business continuity for each site during the migration. In addition to the SD-WAN overlay, firewalls for enhanced security are also part of this deployment.

“It was paramount for us to choose a partner who already had proven experience implementing and operating SD-WAN solutions. Orange Business Services stood out as this trusted partner. In addition to their IT expertise, the Orange teams demonstrated a great ability to understand our business challenges, and they knew what needed to be done to support our end-to-end digital transformation. This close collaboration between our teams from the very beginning of the project was one of the keys to its success and to a smooth roll out,” said Bernard Gavgani, Chief Information Officer at BNP Paribas Group.

“We are delighted to support BNP Paribas in their transformation program and deploy the first large-scale SD-WAN project in the retail banking industry for the French market. An indepth understanding of our customers’ business needs is essential to co-develop customized and innovative solutions. Orange Business Services will continue to accompany BNP Paribas’ central and local teams to learn and develop their SD-WAN skills,” said Nadine Foulon-Belkacémi, Executive Vice President, French Major Clients at Orange Business Services.

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How to ensure you bullet proof your IT in a hybrid finance workplace 

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How to ensure you bullet proof your IT in a hybrid finance workplace  2

By Caleb Mills, Chief Technical Officer at Doherty Associates outlines the dangers faced by finance and private equity firms when it comes to IT infrastructure in a pandemic. Caleb warns that maintaining security is critical as firms continue to work remotely in the current lockdown while making plans to return to the new blended workplace in 2021.

2020 was a year of rapid change – for the technology sector in particular. Virtually overnight, IT firms had to meet the growing demands of many businesses accelerating their technology plans in a bid to stay ahead of the new virtual business environment we suddenly found ourselves in. Covid-19 forced many organisations to automatically relax their security policies so that employees could operate in the remote-only world which followed the UK’s first national lockdown in March.

Can personal devices ever be compliant?

When the announcement of the first March lockdown was made, employees were sent home to work, and largely did so on their personal devices; home PCs, personal mobile devices or shared laptops. Compliance calls for organisational data to be encrypted and kept private, access to be audited and for its transmission to be only over secure channels. Many of these requirements are not met if the use of personal devices is allowed carte blanche – so it’s very likely that some firms are falling short of their compliance obligations.

Added to this is the fact that many employees do not want to allow their organisation to install management software, enforce policies, or limit their freedom on the use of personal devices. They may feel that their company is infringing personal liberties or ‘spying on them’. The most simple and effective (yet costly) solution is to issue company devices for all staff – although there may be some resistance from some to having two devices.

There is an option for controlling company data on personal devices that can satisfy some compliance requirements. Technologies now exist to allow organisational data to be kept in a separate virtual container on the device where policies around encryption and such can be enforced without contravening your employees’ privacy. The company portion of the device can be kept in a secure bubble, without enforcing rules or infringing on individual’s freedom with their own personal devices.

New risks and responsibilities

The accelerated adoption of remote working has meant many risk and compliance teams are still rushing to catch up. Many firms have not thoroughly identified the risks associated with remote or hybrid working, which continue to evolve as the constant demands for businesses change. Even those who have identified risks are likely only considering the ones they understand. In many cases, compliance teams need assistance from a cyber security expert who can help define the risks they are not aware they are taking. An expert will understand the wide and varied attack vectors and provide context and insight into how they could impact risk. The changing environment might call for updates to your IT use policy, cyber security policy, or other IT related policies.

Navigating risk and liability

The approach for managing risk must start by having a clear understanding of what your organisation’s risk appetite is. It is not possible to mitigate or eliminate all risks – there will always be some residual risk and it is important for your organisation to know what level of risk it is willing to accept.

When creating treatment plans for each of your risks, the business should consider the many different angles for controlling and mitigating. There are many technical controls which can enforce your policies, but often organisational controls such as processes or workflows can be just as effective. Choosing to adopt a program like Cyber Essentials can help to ensure that your organisation meets certain requirements. Even the very low bar of its framework can help you to ask pertinent questions about your organisation’s security posture.

Changing security boundaries

In days gone by, businesses took some comfort from knowing they had a secure network. They invested in firewalls to build a border around their network, and they trusted workers and the data they accessed to be protected against security threats. Now, many things have changed.

Data is no longer kept solely on servers in the office, it’s now stored largely in the cloud. And, thanks to Covid-19, many users are now operating outside of this safe and secure network too. The net effect of these two key changes is that the approach of building a highly secure boundary around your network no longer delivers the desired results. The post-pandemic workplace, even more so in finance and private equity, needs to be productive and secure from anywhere in the world.

The modern hacker is not just focused on defeating a firewall – they want to steal your firm’s data – and the way they achieve that is typically to hijack an individual’s identity. Modern security now focuses on protecting the data and the identity of workers by using multiple layers of security controls. This multi-layer, or “onion” approach, works on the assumption that a determined attacker can breach anyone or two layers of security protection. To keep your organisation protected, you should have multiple security controls in place to ensure coverage to help keep your environment safe.

Securing and supervising data rooms in a hybrid world

Data rooms provide a critical function by allowing third party organisations to securely access confidential data, so it’s important that the sensitivity of this is considered before embarking on any data room project. Appropriate policies about how the data should be accessed and used can then be enforced by the technology, and these clearly defined policies will allow for tightly configured security controls to limit access appropriately.

For example, data room guests might be allowed to view documents, but prevented from downloading them or copying and pasting content from them. Modern capabilities even include the ability to “timebomb” documents – for example to block access to documents after an NDA has expired.

Finally, consider taking Cyber Insurance. This can provide help with investigations, guidance on reporting to the ICO, help with public relations and communications, and help cover other expenses incurred as part of a cyber event.

The ongoing events of 2020 have changed the way we work forever. New risks and opportunities have continued to emerge through this period, and it’s ever more apparent that the world will never  go back to how it worked before. Hybrid working is here to stay so we need to understand the implications and take appropriate steps to ensure we meet our compliance obligations and control risk exposure through a mixture of controls to stay ahead of the game.

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