As the volume of data grows and becomes more integral to daily and long term enterprise operations, the need to secure that data, and identify potential threats to it has never been more important. Cybercrimes are on the rise, with supply chain attacks up 79%, and reports of malicious PowerShell scripts increasing 1000%, in 2019 alone.
A snapshot of 2018 fraud statistics presented by the FTC shows that the total identified fraud losses for individual Americans was about $1.48 billion, with the median loss per reportant equaling $375. And these statistics only reflect a portion of the 1.4 million total known instances of fraud, 75% of which did not result in any financial loss.
For institutions, fraud threats can be financially devastating, resulting in losses due to theft, misrepresented financial reports, settlement costs and legal fees, operational disruption, and damaged customer and business relationships.
Fraudsters are leveraging powerful, and widely available tech tools to mine businesses and individuals for important data with malicious intent, and it is incumbent that financial institutions apply the most advanced tech tools for combatting this.
Promoting Customer Security with AI
Most financial institutions use rule-based algorithms for alerting and blocking the potentially fraudulent transactions within all customer accounts. While this method has long been a sufficient mode of identifying fraudulent spending, the advancement of the digital age, and the expansion of mobile banking, means that individuals are keeping their money in a growingly diverse number of places, and routinely make transactions all over the world from single locations. This change has made it difficult for companies to employ one-size-fits-all predictive models to secure all customer accounts, since the diversity in user profiles and legitimate financial behaviors has outgrown them.
Artificial Intelligence, however, is making it possible for financial institutions to create dynamic user profiles that track the behavior of individual account holders, and tailor fraud markers to their unique spending patterns. Kount, one company that has deployed its own proprietary AI model, for example, has found that their payments fraud detection accuracy has doubled when compared to predictive models, all the while maintaining a response rate of less than half a second.
Implementing a successful AI fraud detection solution not only increases the volume of accurate detection, but decreases the instances of false positives, saving financial institutions the time and resources expended on human fraud analysis, falsely frozen accounts, and the need to constantly update predictive models.
Protecting Data in the New Digital Age
In late 2019, RiskedBased Security released a mid year report claiming that 2019 was on track to be the worst year for breach activity, with over 4.1 billion records compromised in the first 6 months.
When we look at the total number of exposed records that same year, 61.7% belonged to financial institutions, including American Express, Suntrust, Capital One, Discover, and Lincoln Financial. Although falling victim to only 6.5% of all reported security breaches, financial companies tend to be most devastated by attacks due to the volume, variance, and sensitivity of their records when compared to that of other businesses.
In the advanced digital age, data is not only crucial to protect for the interest of your customers, but also to prevent the distribution and corruption of information used to propel crucial operational decisions. Companies do not only have the responsibility to protect their customers against identity theft and fraudulent spending, but must also promote their own interests by safeguarding their most valuable digital assets.
Financial institutions are massive, and as newer technologies are introduced alongside more antique, but nonetheless useful software and hardware, the challenge of protecting sensitive information at scale grows with every passing day. And just as our technologies are diversifying, so too are the threats against it.
By implementing AI technologies, companies no longer need to develop seemingly endless and rapidly evolving solutions to potential security threats. Instead, companies can mobilize their existing data to create or implement intuitive softwares, able to identify real time security threats, and take appropriate action both with and without human intervention. How to Start Your AI Journey in 2020
According to IDC predictions, worldwide AI spending in 2019 was expected to be 44% higher when compared to data from the previous year. Further analysis also suggests that the banking sector is the second highest AI investor, with total spending believed to fall around $5.6 billion.
With the emergence of blockchain technology threatening to dramatically revolutionize the banking sector, financial services companies will need to maintain their appeal to individuals and enterprises by leveraging their data to create more secure, performant products, services, and experiences.
AI’s use in promoting data security is only one of the myriad applications of this transformative asset, expected to drive global trade operations as we enter a new era of advanced digital technologies. AI and Machine Learning are both tools that financial institutions must use to improve customer experiences, uncover novel business opportunities, and drive the direction of their future operations.
The journey to full digital transformation is one of small, incremental steps. Financial services companies interested in AI integration do not have to start by creating full fledged, proprietary security systems, but can invest in future success by preparing their data, and implementing a simple AI based foundation for handling internal functions before ramping their technologies up to handle more critical aspects of their enterprise operations.
Take this opportunity to set your company up for success as our digital capabilities grow both in capacity and necessity for modern global trade. By working with This Dot Labs, financial institutions learn more about how AI can support their unique operational needs, plan a pathway for its place in their future technical programs, and even start implementing some of the world’s most cutting edge technologies into your daily workflows.
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