While the finance and banking sector has a reputation of maintaining a relatively conservative, cautious approach toward major disruptions, the current push for digital transformation, CX prioritization, and data-driven automation has been leading to massive changes. AI integration is one of them.
Chatbots and CX augmentations may well be the headline-makers of today, and a likely cutting-edge tech priority of the future, but right now risk management and regulatory compliance are #1 when it comes to funding. According to this year’s AI in Banking report, risk, safety and compliance AI alone currently accounts for more than half of $3 billion investments, according to the Emerj 2019 report.
Fraud detection and cybersecurity with AI in finance
With a 26% share of the funds raised by AI vendors working in finance (Emerj), fraud protection and cybersecurity are the top current uses and adoption opportunities for risk-related AI and ML in banking and finance.
Pwc’s Global Economic Crime and Fraud Survey 2018 states that 49% of respondent organizations experienced fraudulence within the previous 24 months (a 19% increase over the last decade). The newest Juniper research on online payment fraud marks banking among the most popular fields for financial fraud, too. This makes advanced detection and prevention systems a necessity for banking loss minimization.
New ML-based real-time fraud detection systems, AI integration, and upgrade of legacy solutions are necessary to respond to the current challenges in the finance sector. The core differences between traditional anti-fraud systems and methods is that AI and ML algorithms are faster, can adapt to change, process versatile, big(ger) data on the go, and are specifically designed to autonomously detect new patterns and catch transactional anomalies.
Here are three major opportunities for artificial intelligence and machine learning in finance and banking.
Preventing transaction fraud and minimizing false declines
In the US alone, annual losses attributed to credit card fraud are predicted to surpass $12B in 2020 (Forbes), demonstrating that the existing systems leave a lot of room for crime as well as untapped possibilities for its identification.
A classic card fraud detection system is essentially a fixed set of what-if rules to check and evaluate card transactions, both online and offline. Its strengths and limitations stem from the same source ”it is pre-programmed and can only mark cases identical to those previously discovered and broken down into a rigid number of checkboxes.
Machine learning and AI-based systems are more flexible, adaptable to new and previously unidentified fraud methods, can pull data from various changing datasets and compare thousands of data points. Built for holistically monitoring clients’ digital profiles, this technology can also help service providers minimize false positives in the existing systems.
Fighting insider fraud with AI
Motifs and violation of trust-related questions aside, risk management related to corporate sabotage is a universal priority in finance and banking where up to 70% of fraud is committed by insiders, including employees.
From a technological point of view, this type of financial crime can be mitigated with AI integration into existing business intelligence structures and employee management solutions. Such automated and adaptable solutions can identify unusual behavior with out-of-standard-routine patterns, rate and signal suspicious actions to higher-level management. All this is done in the broader context of internal monitoring, augmented business analytics, audits, and predictions.
Combating money laundering
It is estimated that money laundering annually amounts to 2 “5% of the global GDP (UNODC). Traditional anti-money laundering (AML) approaches and compliance frameworks generally show poor results despite steady investments and customary state endorsement. Several factors contribute to this, including high numbers of false positives (up to 95-98%) due to rules thresholds, which distracts compliance teams from truly fraudulent cases.
There are several ways AI and ML can drive the efficiency of AML checks:
- ML algorithms can be used to detect and risk-rate suspicious activity based on multiple and changing sets of entry points.
- Trained on a combination of historical outputs of established systems, AI can learn to automatically recognize particular anomalies and categorize them as suspicious characteristics of financial behavior.
- For advanced name screening, ML algorithms can navigate and interpret various banking systems, like those with problematic data sets (in regional-specific formats, for example).
- Self-improving and constantly updating algorithms can continuously reevaluate matching criteria and perfect name match predictions.
All of the above have the potential to dramatically lower the percentage of false positives. Additionally, instead of spending the majority of time on repetitive tasks, compliance specialists will be able to focus on high-value issues, ensure continuous review, and update classifications for detected activities.
Wrap-up
Against the backdrop of industry traditionalism and ongoing global regulatory changes, AI adoption in finance is steadily growing. This indicates the clear and assured future for AI in the sector.
The threat of fraud in its various manifestations has always been prevalent in finance and banking. Rapid technological advancements of the last decade brought about new outlets for fraud and cybercrime alongside the existing ones. In this environment, utilizing AI and other cutting-edge technology is not only advantageous but critical in the ongoing war against fast-evolving fraudulent schemes.
