Connect with us

Global Banking and Finance Review is an online platform offering news, analysis, and opinion on the latest trends, developments, and innovations in the banking and finance industry worldwide. The platform covers a diverse range of topics, including banking, insurance, investment, wealth management, fintech, and regulatory issues. The website publishes news, press releases, opinion and advertorials on various financial organizations, products and services which are commissioned from various Companies, Organizations, PR agencies, Bloggers etc. These commissioned articles are commercial in nature. This is not to be considered as financial advice and should be considered only for information purposes. It does not reflect the views or opinion of our website and is not to be considered an endorsement or a recommendation. We cannot guarantee the accuracy or applicability of any information provided with respect to your individual or personal circumstances. Please seek Professional advice from a qualified professional before making any financial decisions. We link to various third-party websites, affiliate sales networks, and to our advertising partners websites. When you view or click on certain links available on our articles, our partners may compensate us for displaying the content to you or make a purchase or fill a form. This will not incur any additional charges to you. To make things simpler for you to identity or distinguish advertised or sponsored articles or links, you may consider all articles or links hosted on our site as a commercial article placement. We will not be responsible for any loss you may suffer as a result of any omission or inaccuracy on the website. .


How Machine Learning is transforming the way in which financial institutions approach risk management

How Machine Learning is transforming the way in which financial institutions approach risk management

Jason Robson is Head of Software Development at Equiniti Riskfactor

Machine Learning (ML) is a branch of the more commonly understood field of Artificial Intelligence (AI), the subject of many Hollywood dystopian ‘rise-of-the-machines’ style movies.

In essence, Artificial Intelligence attempts to mimic human intelligence or behaviours. Machine Learning attempts to analyse and associate patterns of behaviour in diverse data sets to support data-driven decision making based on new knowledge and understanding.

Traditional risk models have used statistical or expert-driven heuristics, but now the next generation of risk analytics is taking advantage of the work being done in this growing field of Data Science.

As fraud is thankfully a relatively a rare occurrence within an organisation, developing simulation tools is key to understanding the lifecycle of a fraud. Using real world examples, we are now able to model the patterns of behaviour surrounding a fraud in order to reproduce the event with diverse sets of changing dynamics. This allows us to represent and understand the fraud over a range of time periods and with utilising differing levels of funding.

Most of the work of a Data Scientist is at this (slightly unglamorous) end of the workflow – essentially the acquisition of test data and its transformation into more suitable forms for use in data analytics.

‘Data Munging’ is the delightful phrase that has been given to this activity.

Aside from a background in probability and statistics, the Data Scientist’s toolbox consists of technologies such as the programming languages Python and R, which can be tailored to accommodate statistical computing and graphics.

Cloud computing providers such as Microsoft Azure and Amazon also have services dedicated to Machine Learning problem domains.

Machine Learning algorithms allow the matching of patterns and connections that can’t be expressed easily, or even at all, by people. Imagine the field of speech recognition, where devices from Google, Amazon and Apple can not only identify what is being said, but which person in a household is saying it.

The unique patterns of speech can be recognised even though the reasons why could never be easily conveyed to its owner in words. Now swap the rises and falls in pitch and amplitude with time series metrics derived from a commercial finance facility, and you will immediately see the future possibilities we are exploring.

The abundance of data that surrounds us covers not only our work lives and business connections, but also information about our social interests and friends. This rich picture will play a hugely important role in fully understanding the events we wish to model.

The wealth of data in the world we inhabit today is moving the bar above mere fraud detection,towards future fraud prediction. And yes, if you are thinking ‘Minority Report’, Hollywood does seem to have got there first).

Global Banking & Finance Review


Why waste money on news and opinions when you can access them for free?

Take advantage of our newsletter subscription and stay informed on the go!

By submitting this form, you are consenting to receive marketing emails from: Global Banking & Finance Review │ Banking │ Finance │ Technology. You can revoke your consent to receive emails at any time by using the SafeUnsubscribe® link, found at the bottom of every email. Emails are serviced by Constant Contact

Recent Post