Artificial Intelligence(AI) and Machine Learning (ML) have become a part of our everyday lives. The technologies are even present in some of our daily activities without us realizing it. Social media news feeds, facial recognition software and even the music and tv shows that are recommended to us on streaming platforms, all utilize AI and ML in some form or another.
Machine Learning in Finance
The corporate world is continually changing and evolving. These changes have been accelerated due to the implementation of these technologies.
Machine learning analyzes historical data and user behavior to find and predict patterns and accordingly make decisions. This ability has been very successful in the retail industry as it gives businesses the opportunity to customize products and services to their customers’ preferences.
Machine learning in retail banking is also a great combination. Machine learning tools can be used to automate fraud detection as well as credit scoring. Banks can implement machine learning and predictive analytics to offer the clients a highly customized user experience, recommend new services and products or even animate chatbots to help with everyday transactions like checking account balance or paying bills.
Machine learning has also found several applications in the insurance sector. As the number of connected devices increases, ML applications are able to derive deeper insights into customer behavior and insurers can set premiums and make decisions regarding payouts based on that data.
There are very few industries that have as much historical and structured data as the financial services industry. This makes it ideal for utilizing machine learning technologies.
One of the first applications of AI technology in finance was in investment banking. Nowadays, fund managers and traders depend on AI-driven market analysis to make investment decisions. This has opened the doors for other financial institutions to develop new digital solutions for financial trading using AI-based technologies.
AI-powered solutions like stock ranking based on pattern matching and deep learning for developing investment strategies is just one example of the number of innovative solutions available in the market today.
Despite these promising technological advances, we are still a long way off from machine learning applications being able to completely replace humans and human interactions for financial trading.
Although index and quantitative investing accounts for more than half of all equity trading, poor performances have exposed vulnerabilities in the pattern matching model upon which investment strategies are based. It also demonstrates that no matter how complex the math, computers are still not a viable replacement for the human mind when it comes to tackling the nuances of the financial markets. Well not yet, at least.
Data Analytics for Security and Compliance
Due to the large volumes of data, they have to handle, compliance and security risks are two of the biggest challenges financial organizations face.
The thought that securing your network perimeter is enough to protect it from attacks is not enough, exponential growth of data and increased legitimate access to data increases the chances of a breach occurring from the inside.
Moreover, banks store huge volumes of data across hybrid and multi-cloud environments that give even more opportunities for hackers to gain access to valuable assets. Basically, the same data that creates new opportunities for the growth of the business also increases the security threats for financial organizations.
Data analytics using ML has been key in enabling firms to overcome such challenges as it detects unusual user behavior by constantly searching for any suspicious activity and therefore, significantly decreases the risk of fraud, money laundering or even, a breach.
Data analytics technologies can also be used to adhere to compliance activities like database auditing processes, lowering the requirement of human intervention and hence, easing the burden for compliance managers.
What to expect in the future
As the financial services industry continues to leverage machine learning and predictive analytics, the volume of data these firms generate and the store is ballooning.
As a result, protecting that data, various sensitive assets and business operations will most likely become even more challenging. Organizations will need to constantly adopt new security technologies that will help them not only mitigate potential security and compliance risks but prevent them from happening altogether.