Changing the face of Financial Services

Contributed by Imperva
Written by Grainne McKeever, Product Marketing Manager at Imperva

Artificial Intelligence (AI)[2] continues to be a hot topic as it becomes more embedded in our day-to-day lives, powering what we see in our social media newsfeed, activating facial recognition (to unlock our smartphones), even suggesting music for us to listen to. These are all examples of how machine learning, a subset of AI, is progressively integrating into our ‘everyday’, changing how we live and make decisions.

Machine Learning in Finance
Business changes all the time but advances in today’s technologies mean the changes are coming at a rapid pace. Machine learning analyzes historical data or behaviors to predict patterns and facilitate decision-making. It has proved hugely successful in retail for its ability to tailor products and services for customers and, unsurprisingly, retail banking and AI are also the perfect combination.  Thanks to machine learning, functions[3] such as fraud detection and credit scoring are now automated, and banks can offer their customers a much more personalized user experience using predictive analytics to recommend new products, and chatbots to help with account checking and paying bills.

Machine learning is also disrupting the insurance sector as a greater number of connected devices provide deeper insights into customer behaviors, enabling insurers to set premiums and make payout decisions. Insurtech[4] firms are shaking things up in this traditional industry by harnessing new technologies to develop enhanced solutions for customers.   The potential for change is huge and, according to McKinsey, ‘the (Insurance) industry is on the verge of a seismic, tech-driven shift’.[5]

Financial Trading
Few industries have as much historical and structured data, making financial services the perfect playing field for machine learning technologies. Investment banks were pioneers of AI technologies using machine learning since as early as the 1980s[6] and currently traders and fund managers rely on AI-driven market analyses to make investment decisions, thus paving the way for fintech companies to develop new digital solutions for financial trading.  AI-driven solutions such as stock-ranking based on pattern matching and deep learning for formulating investment strategies are just some of the innovations available on the market today.[7]

Despite these incredible technological advances, the concept of machine learning replacing human interaction for financial trading is not a done deal. While index and quantitative investing account for over half of all equity trading[8], poor performance in recent months has exposed weaknesses in the pattern matching model on which investing strategies are based and demonstrates that, no matter how fancy the math, or how well thought out the investing strategy, computers are still no replacement for the human mind when it comes to capturing the nuances of a particular set of market conditions.

Data Analytics for Security and Compliance
Managing enormous volumes of data makes compliance and security two of the biggest challenges for financial organizations.  It is no longer enough to protect the network perimeter from attack, as the exponential growth of data and an increase in legitimate access to that data, increases the likelihood of a breach on the inside.  In addition to that, banks are storing large volumes of data across hybrid and/or multi-cloud environments.  This provides even more opportunity for cybercriminals to get their hands on the firm’s valuable assets. In short, the same data that brings new opportunities for business growth also increases the security risk for financial firms.

“The biggest value of using AI is its capability to understand and predict patterns in risk; manage the data; and gain insights on the data”[9]

Data analytics using machine learning has been transformational in helping firms overcome these challenges as they identify unusual user behavior to detect suspicious activity and minimize the risk of fraud, money laundering, or a breach. Similarly for compliance, data analytics technologies can be applied to database auditing processes, reducing the need for human intervention and thereby easing the burden for compliance managers.

Looking Ahead
As growing data volumes cause the cyberthreat landscape to expand and regulatory controls to tighten, protecting business operations and sensitive assets will become more challenging for firms who continue to seek out new technologies to mitigate security and compliance risk.

Financial services is one of many industries investing heavily in AI technology and spending on AI systems is expected to reach $35.8 billion in 2019 and to more than double to $79.2 billion by 2022[10].

With the emergence of deep learning and further advancements in AI, who knows how far this will go? For financial organizations, AI technologies and machine learning will continue to drive innovation, automation and transformation, resulting in a much more efficient operating model for the industry.
About Imperva
Recognized by industry analysts as a cybersecurity leader, Imperva champions the fight to secure data and applications wherever they reside. In today’s fast-moving cybersecurity landscape, your assets require continuous protection, but analyzing every emerging threat taxes your time and resources. For security to work, it has to work for you. By accurately detecting and effectively blocking incoming threats, we empower you to manage critical risks, so you never have to choose between innovating for your customers and protecting what matters most. At Imperva, we tirelessly defend your business as it grows, giving you clarity for today and confidence for tomorrow. Imperva—Protect the pulse of your business.
[1] Featured Image: By Gerd Altmann from Pixabay



[4] Insurtech refers to the use of technology innovations designed to squeeze out savings and efficiency from the current insurance industry model. Insurtech is a combination of the words “insurance” and “technology,” inspired by the term fintech. – Investopedia







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