How to use Machine Learning and Artificial Intelligence to mitigate digital risk

Machine Learning (ML) and Artificial Intelligence (AI) are a huge part of digital transformation. More and more organizations are deploying ML and AI applications to save time, boost revenue, increase value to their customers and most importantly identify and prevent cyber risks.  This overview highlights how to use Machine Learning and Artificial Intelligence to mitigate digital risk.

2019 marked the 30th anniversary of the World Wide Web, three decades marked with massive disruption and the dawn of “digital transformation”. The ever increasing reliance on the volatile, hyper-connected nature of digital technology in business and life has greatly amplified digital risk. In its recently published annual report on Cyber Security Breaches Survey the UK Department for Digital, Culture, Media and Sport (DCMS) disclosed that 61% of large businesses in the UK had experienced an attempted cyber breach in the preceding 12 months, some admittedly once a week[1].

On average, the financial cost of a cybersecurity attack with a negative outcome is £22,700, less the costs of loss of business continuity or reputational damage[2].

Deploying machine learning and artificial intelligence solutions in risk management opens the door to a plethora of opportunities including cost reductions of 25 percent or more. So, let’s find out how AI solutions can be incorporated to overcome the risk cases.

Password Protection and Authentication

For years the passwords have been the holy grail of cyber security, often the only line of defense standing between our accounts and cybercriminals. However, passwords are becoming security liabilities because of how we store them and the increased sophistication of cybercriminals.

Today the AI-powered biometric systems are making passwords obsolete. A classic case in study is Apple’s face recognition technology, used on its iPhone X devices. Called ‘Face ID,’ the technology works by processing the user’s facial features through built-in infra-red sensors and neural engines. Apple claims that with this technology there’s only one-in-a-million chance of fooling the AI and opening your device with another face [3]. By creating a sophisticated model using correlations and patterns Apple’s AI software is able to correctly identify the face of the user. The software architecture of the software is adaptable and designed to function in different lighting conditions such that it can accommodate changes in users’ face and dressing.

Phishing Detection and Prevention Control

Phishing is arguable the number one method used by cyber-criminals in their malicious activities. It is estimated that 1 in every 99 emails comes from cybercriminals[4].

AI-ML solutions are very efficient in detecting and tracking suspicious emails and websites at the minimum they can efficiently diagnose more than 10,000 active phishing sources from anywhere around the globe and immediately react to neutralize them. Moreover AI has also made it possible to rapidly differeciate between a fake website and a legitimate.

Automated Network Analysis

Majority of cyber-attacks happen over the network, hence having a good front line defense system is imperative. AI powered systems can be deployed for anomaly detection, keyword matching, and data monitoring. The AI systems alert typically will alert you to any suspicious communications that have sneaked passed your firewall.

Creating security policy and establishing an organization’s network topography are two very important parts of network security system. AI solutions are now been deployed to accelerate and expedite both processes. By observing network traffic patterns machine learning is able to learn the traffic patterns and suggest security policies.

The Bottom Line

Investment in AI and machine learning capabilities have lots of potential for the European market, McKinsey has forecasted that an extra €900 billion contribution to GDP bringing the total potential AI boost to €3.6 trillion by 2030[5].

As the race to digital intensifies, speed of adoption and innovation will be a critical determinant of who the next frontiers will be.

The Digital Risk Conference in London on 19-20 May 2020  is a great avenue for you to catch up with the latest trends on how to use Machine Learning and Artificial Intelligence to mitigate digital risk. The theme of the conference is managing risk and security in digital age and attendees are 16 banks, International Banking Federation, Financial Services and Markets Authority, insurance companies and other players in the BFSI arena.

Conference website:









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