Dr. Mark Goldspink is the company’s CEO Ai Corporation (ai), they offer unique self – service fraud detection solutions that many industry “thinkers” consider to be the best in the world. Solutions, including a new “state of the art” machine learning protect and enrich the payment experiences of more than 100 banks, more than three million multi-channel merchants and more than 300 million consumer cardholders. They also follow more than 25 billion transactions and promises a year.

What originally attracted machine learning and artificial intelligence?

Artificial intelligence is the only way to make business decisions with large sources of information effectively.

My first review of machine learning (1992) was price elasticity in the fuel industry. Each gas station and product has its own elasticity index. Using artificial intelligence, we made a proactive pricing tool that helped us understand the ratio of the price to the quantity requested. This tool is still used all over the world.

Could you briefly explain what ai Corporation (ai) is?

ai facilitate the digitization of payment solutions by providing enterprise cloud products and services. Simply put, we aim to secure our customers ’payment technology in the future to ensure they stay in line with the broader, ever-evolving digital marketplace. We believe that payments should become invisible so that companies can focus more on managing their customers and spend less time taking care of how payments are made.

What machine learning techniques are used to report potential threats and fraud?

Many different methods and techniques are used in a fraud and threat detection environment. Early examples are Bayesian logic and decision trees. Subsequent innovation led to the widespread adoption of neural networks, which are still largely used today. The rapid introduction of machine learning (ML) in all industries has further encouraged the use of several other methods, such as unattended technology, XGBoost, and others. Most companies decide to develop their own methods, which are usually based on existing methods to improve detection efficiency.

Currently, while machine learning does most of the “heavy lifting,” a lot of control is still needed to fight fraud. How can automating the fraud management process with model orchestration solve this problem?

Most fraud systems use multiple models to detect the optimal amount of fraud while reducing the “offensive” amount of legitimate payment bills. However, the number one issue with these systems is the huge amount of extra administration needed to manage these models. That administrative and management work is usually manual, and the more models used, the more manual work is needed to maintain good model performance.

The Model Orchestra is revolutionizing the way we manage fraud systems. With the help of the orchestra, the machine manages itself effectively. When models are found to be malfunctioning, they are replaced with a new model or trained based on the latest data. This process uses automated processes to retrieve performance statistics for live models, as well as the same statistics for all models generated automatically offline. When offline models work well, they are promoted to production. This forms a closed circuit, an automated system for optimal fraud detection and business processes.

Could you give some examples of cybersecurity or fintech threats that have been detected in the past through machine learning?

Here, oh, we are constantly preventing fraud by using the machine learning solution AutoPilotML, which keeps scam trends up to date by automatically optimizing the entire fraud strategy (which consists of both ML and hand-generated rules). Our optimized solutions have detected account holder and account hacking systems in TELCO mode, for example, saving operators thousands of pounds. In fact, we have detected suspicious events worth nearly $ 10,000,000 a year.

Outside of fraud, machine learning prevents major attacks on a daily basis. For example, Microsoft has for years detected and prevented cyber attacks from botnets. In February 2018, they almost immediately blocked a huge Emotet botnet attack) using the so-called Layers of machine learning, consisting of both client- and server-side machine learning models.

Cyber ​​security threats often cause a dark network. Could you discuss some of the Dark Web Managed Services currently available?

ai provides a dark network monitoring service that works through Skurio’s SaaS application to inform and protect compromised customers as soon as their data is exposed to the dark network. We believe the partnership is unique and provides our users with the world’s leading cyber intelligence that automatically scans websites, marketplaces and forums to detect customer information in the dark.

Is there anything else you would like to share about The ai Corporation (ai)?

Building great products along with the right processes can only be achieved if we have a world class team. I am very proud of the team, and teamwork helps us to be innovative.

It’s also teamwork that makes working enjoyable and motivating for me. It’s the most rewarding thing about being a leader. Helps people develop and grow.

Thanks for a great interview, readers who want to learn more should visit Ai Corporation (ai).


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