Artificial intelligence (AI) has become integrated into our everyday lives. It powers what we see in our social media newsfeeds, activates facial recognition (to unlock our smartphones), and even suggests music for us to listen to. Machine learning, a subset of AI, is progressively integrating into our everyday and changing how we live and make decisions.
Machine Learning in Finance
Business changes all the time, but advances in today’s technologies have accelerated the pace of change. Machine learning analyzes historical data and behaviors to predict patterns and make decisions. It has proved hugely successful in retail for its ability to tailor products and services to customers. Unsurprisingly, retail banking and machine learning are also a perfect combination. Thanks to machine learning, functions such as fraud detection and credit scoring are now automated. Banks also leverage machine learning and predictive analytics to offer their customers a much more personalized user experience, recommend new products, and animate chatbots that help with routine transactions such as account checking and paying bills.
Machine learning is also disrupting the insurance sector. As more connected devices provide deeper insights into customer behaviors, insurers are enabled to set premiums and make payout decisions based on data. Insurtech firms are shaking things up 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.”
Few industries have as much historical and structured data than the financial services industry, making it the perfect playing field for machine learning technologies. Investment banks were pioneers of AI technologies, using machine learning since as early as the 1980s. Nowadays, traders and fund managers rely on AI-driven market analysis to make investment decisions that are 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.
Despite these 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, recent poor performance has exposed weaknesses in the pattern matching model on which investing strategies are based and demonstrates that, no matter how fancy the math, computers are still no replacement for the human mind when it comes to capturing the nuances of financial markets. At least, not yet.
Data Analytics for Security and Compliance
Managing enormous volumes of data make compliance and security two of the biggest challenges for financial organizations. It is no longer enough to protect your 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. Additionally, banks are storing large volumes of data across hybrid and multi-cloud environments that provide even more opportunity for cybercriminals to get their hands on valuable assets. In short, the same data that brings new opportunities for business growth increases the security risk for financial firms.
Data analytics using machine learning has been transformational in helping firms overcome these challenges as it picks up on unusual user behavior to detect suspicious activity and minimize the risk of fraud, money laundering, or a breach. Similarly, data analytics technologies can be applied to compliance activities such as database auditing processes, reducing the need for human intervention and thereby easing the burden for compliance managers.
As the financial services industry continues to leverage machine learning and predictive analytics, the volume of data these firms generate and store is ballooning. Protecting that data, other sensitive assets, and business operations will only become more challenging. Firms will have to adopt new security technologies that can mitigate their security and compliance risk.
How Imperva Can Help
Imperva Data Security helps companies mitigate the risk of a security breach, internal and external, by using machine learning and data risk analytics to identify suspicious data access and prioritize threats. Imperva establishes a baseline of typical user access to database tables and files, then detects and alerts you to abnormal behavior before they become actual threats. Distilling millions of alerts to let only the most urgent threats bubble to the surface lets security teams focus on high-risk incidents. Imperva Data Activity Monitoring (DAM) provides enterprise-wide visibility across multiple storage locations, on-prem and in the cloud, and into all database transactions, to create granular audit trails that pinpoint the “who, what, when, where and how” for each database.
Imperva Application Security prevents operational downtime by providing a full-stack application security solution to protect your websites and APIs and to ensure systems operations remain functioning and available for your customers. With integrated Cloud WAF, CDN, DDoS protection and Attack Analytics, plus Bot Management and Runtime Application Self-Protection (RASP), your business will be protected on the inside as well as at the edge, offering a true defense-in-depth solution
To find out more about how Imperva can protect your business, visit our website: www.imperva.com.