Feature
Managing risk is an ever-changing challenge. Threats come at companies from all directions. Keeping up with the risk caused by these threats means staying on top of a vast river of always moving data. Whether your business is concerned with compliance, regulatory changes, cyber threats, data breaches or financial dangers, artificial intelligence (AI) can quickly analyze data, recognize patterns and stay on top of vast amounts of relevant information. AI is increasingly being used in risk management to speed up decision making, build forecasts based on richer data sets and detect fraud. Here, we look at several examples of risk management that illustrate the ways that AI is changing this data-heavy segment.
1. RAZE Banking
At RAZE Banking, traditional risk management methods were failing to keep up with the ever-changing world of cyber threats, compliance issues and operational risk, according to a case study. After experiencing a surge in fraud that cost the bank both financially and reputationally, the bank worked with RTS Labs, which develops AI solutions, to build a better risk-mitigation strategy.
The solution RTS designed used predictive analytics, harnessed historical data and identified patterns to identify likely fraudulent activities amid a sea of transactions. This allowed the bank to anticipate risk rather than merely react to it. The model learned continuously, based on the new data it acquired, so the predictive ability grew as the risks the bank faced morphed.
“This system seamlessly integrated with the bank’s existing infrastructure,” according to RTS Labs, “enabling real-time analysis of transactions, customer behavior and regulatory compliance.”
Results
- Saw a 45% reduction in fraudulent transactions
- Experienced a 20% improvement in regulatory compliance efficiency
- Operation efficiency improved by 30%
2. Network International
Network International is an enabler of digital commerce across the Middle East and Africa, providing a range of payment solutions to merchants and banks. This is a geographic region that still clings to cash as a payment solution but that is beginning to change. This development brought with it a need for better fraud protection. Like many financial institutions in the region, Network International was using a rules-based system that was not up to the demands of a huge number of digital transactions, according to a case study.
“Fraud solutions must be increasingly sophisticated in order to provide the level of protection that cardholders expect,” says Navneet Dave, managing director and co-head of processing of Middle East, Network International.
To improve its speed at fraud detection even as transactions increased, the company turned to FICO and its Falcon Fraud Manager for a system that used advanced analytics, AI and machine learning (ML) to enable real-time payments while keeping up with rapid increases in fraud. The system allowed Network International to stay up to date on fraud trends, predict fraud rather than react when it happens and better protect customers’ money and data.
Results
- Provided fraud prevention specific to client’s geography and scale
- Client can deliver real-time fraud detection to customers
- Client can support large and small clients in many countries and evolve with customer demand
3. TowneBank
With a lean staff and an in-progress tech stack, TowneBank was facing a challenging compliance deadline, according to a case study.
In order to keep up with the complex demands of the looming expected credit loss (CECL) accounting standard, the Virginia-based financial institution turned to SAS for an AI-driven framework to help it better manage regulatory compliance and risk. It needed a tool that could securely access data from multiple departments, analyze 15 years of historical and forecasting data and perform complex statistical models. The SaS tools worked well for this.
The bank also found that once it adopted this intelligent system for managing data and predicting risk, it was able to use it for more than this one regulatory issue.
“With 15 years worth of data sitting in our SAS warehouse, we’ve been able to quickly answer questions throughout the organization,” says Erich Reuter, EVP of quantitative analytics and enterprise stress testing, TowneBank. “That’s really allowed us to take the framework that we’ve built for one purpose and use it for other purposes.”
The new system could tap TowneBank’s data, with natural language questions, to answer queries about everything from high-level decisions to understanding individual member needs.
Results
- Supported digital transformation
- Customer service representatives could access thousands of data points to anticipate customer needs
- Bank prepared for CECL and used the same system to access data for a range of problems and questions
4. Mastercard
Mastercard is one of the largest payment systems in the world. Its ecosystem includes partners and vendors as well as third-party contracts. But a fourth tier of processors, digital wallet operators and payment facilitators have become part of Mastercard’s ecosystem, bringing with them increased risk around data security, fraud and compliance with Mastercard’s rules, according to a case study.
To deal with this more distant tier, the company turned to the AI-powered MetricStream Platform, running on AWS, to manage third- and fourth-party risk management.
This smart new system gave Mastercard comprehensive visibility into the risks of these fourth parties and faster risk assessment when dealing with them, even in a vast ecosystem of small service providers and a seemingly infinite number of transactions.
Each entity was categorized based on the level of risk exposure it brought to the ecosystem, so that MasterCard could make fast, informed decisions about each one. It could then accurately target areas of concern and focus its risk-mitigation efforts based on the risk level assigned to each fourth party.
Results
- Reduce time to assess third-party risk by 66%
- Reduced risk exposure from third and fourth parties
- Kept stakeholders in ecosystem informed about the status of fourth-party risk profiles and escalated relevant cases
5. Grupo Bimbo
Grupo Bimbo is a major consumer goods company with a global reputation for creating baked goods. With vast stores of sensitive data — recipes, R&D data, employee data and inventory and production data — in so many countries and a reputation that is valuable to the brand, the company sought to improve its compliance with regulations across those geographies and protect its data and security, according to a case study.
“Compliance with continually increasing and more complex regulations isn’t just part of our competitive advantage,” says Jose Antonio Parra, VP of global digital transformation, data and analytics, Grupo Bimbo. “The reputation of our company is at stake.”
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Grupo Bimbo turned to the AI-powered Microsoft Purview suite of tools to improve its data security, compliance and cybersecurity efficiency. In addition to getting a better handle on what data the company had and where it was located, the tools allowed the company to be proactive and predictive about all forms of risk. The team received alerts about a potential data leak or loss.
“People tend to think that a bakery wouldn’t be that complex,” Parra says. “But any data mishap could affect our consumers, our suppliers and our relationships with regulators anywhere in the world.”
Results
- Deployed the solution to 35,000 associates within 18 months
- Protected all types of data, including confidential and regulated employee health data
- Protected the privacy and data of 145,000 employees and anonymized employee identities in internal communications
