Financial firms are now embracing technology such as artificial intelligence (AI) to secure their network infrastructures. The question is whether AI for financial institutions is worth the effort and money to implement.In May 2018, cyber hackers targeted two of Canada's largest financial institutions, stealing data from 90,000 customers. Officials report that the anonymous cyber-criminals actually contacted them about the network intrusion.
The Case For Artificial Intelligence For Financial Institutions
AI is emerging as a viable solution against cyber breaches. Financial institutions are investing in AI-based technologies at a record pace. For example, JPMorgan's 2018 set aside $5 billion for new security technologies (including AI) for this year.
A new Accenture study revealed almost 75 percent of financial executives believe that intelligent technologies will utterly revolutionize the financial industry. Financial firms that invest in AI are poised to see a 34 percent increase in revenues. Most significantly, employees of financial firms are ready for the AI revolution: 67 percent expect intelligent technologies to create further work opportunities for them.
The Threat Landscape for Financial Institutions
Financial institutions are battling against the greatest security threats of our modern age. Here are some of them:
Ransomware: Hackers use this malware variant to extort ransoms from vulnerable financial institutions. They gain remote control of file servers, commence stealth encryption, and block immediate access to internal networks. Data files can only be retrieved after the required payment is sent. In today's increasingly digitized environment, cyber hackers have begun targeting mobile banking users.
For example, the Svpeng Trojan infects mobile devices through malicious attachments and downloads. When an unsuspecting user opens an app, the malware launches an overlay login site to steal customer credentials. Failing that, the Svpeng malware executes an old-fashioned ransomware attack, where victims must pay to regain device access. In 2017, Sypeng acquired keylogger capabilities, using Android accessibility services to gain sovereign administrator's rights over mobile devices.
Spear Phishing: Due to their personalized nature, spear-phishing attacks are notoriously difficult to prevent. Cyber criminals are adept at compromising social media accounts without leaving incriminating digital footprints. In 2017, the Silence Trojan brought financial institutions in Russia and South East Asia to their knees.
Once infected, droppers (hostile programs that contain viruses) executed malicious payloads on victims' computers and sent server IDs to hacker networks. These payloads allowed hackers to monitor victims' networks and execute screen activity gathering tasks. From 2013 to 2018, Carbanak (a cyber-criminal organization) used spear phishing to net more than 1 billion dollars from over 100 financial institutions.
Anti-Money Laundering (AML) Monitoring Systems: Many of our financial institutions still rely on legacy AML systems. We tolerate false positives because we think they're an intrinsic part of network management. However, massive amounts of false positives are also hampering our ability to contain today's multi-faceted money-laundering schemes. By current estimates, about 90 to 95 percent of AML alerts are false positives. Conventional AML systems simply don't possess the requisite granularity to analyze a plethora of organized and unorganized data.
IoT Botnets: Expanding IoT ecosystems have increased the number of network attack vectors around the world. In early 2018, several financial institutions suffered a massive botnet-facilitated Distributed Denial of Service attack (DDoS).
AI can help financial institutions guard against these threats while still providing a high level of service to customers.
The Benefits Of AI For Financial Institutions
Financial firms that would usually shy away from integrating new technology are opening up to as many viable options as possible to protect against the constant onslaught of attempts to breach the network. Below, we reveal 3 advantages of leveraging AI for your financial institution.
Continuous threat monitoring: AI delivers automated, seamless, and constant monitoring of all structured and unstructured data in your network. It leverages predictive analytics and behavioral whitelisting to uncover the most vulnerable threat vectors in your network defenses. AI enables you to gain unparalleled insight into your network and to design effective mitigation procedures against potential compromises.
Advanced AML risk management and fraud prevention: In the era of the IoT, AI can help you detect behavioral anomalies that characterize fraudulent activity. It particularly addresses the financial industry's vulnerability to spear-phishing and malware-based fiscal theft. Today, some AI technologies are equipped with associative memory capabilities that help provide additional insights into future threats.
Decreased Overhead Expenses From False Positives: AI eliminates the tedium of wading through a mountain of SARS (suspicious activity reports) generated by seemingly overzealous AML systems.
Here’s a look at how to explore AI for your organization.
3 Key Steps To Implementing AI For Your Financial Firm
You understand all too well that the key to success is action. And that's not all: your honed instinct tells you that momentum is important. That said, these 3 steps will boost your beginning efforts to transform your institution's network infrastructure.
- Assess The Immediate Security Needs Of Your Financial Institution: Carefully evaluate your financial institution's pain points. Do you need to secure your networks against fraud? Have you begun offering mobile access to accounts through apps and so, must protect against ransomware and botnet armies that drain customer accounts? Or, are spear-phishing attacks your greatest potential vulnerability?
- Clarify Your Desired Path Of Action: Consider whether your technology budget can accommodate new investments. Decide which KPIs you will use to measure the ROI from an AI investment. Next, decide whether you will invest in a full machine learning AI or a hybrid model. Industry experts at MIT claim that their new AI platform (which incorporates continuous insight from human experts) reduces false positives by a factor of five. Will hybrid models eclipse conventional AI solutions in the future?
- Partner With A Trusted Data Security Vendor: Financial firms should understand the implications of applying a new technology. Financial institutions should apply the proper network security practices and setup obstacles for malicious players.