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How effective is your fraud prevention software?

Most financial institutions use data mining (92.5%) or business rules management systems (BRMS) (65%) to address fraud, yet they say the effectiveness of these two solutions is only 28 percent and 18 percent respectively.

In our ongoing research series about AI use by financial institutions, AI Innovation Playbook: AI and Fraud reports on the experiences of more than 200 financial executives regarding the use of AI to prevent and detect fraud. The adoption rates are surprisingly low considering the potential for success, so we asked why.

Most agreed that artificial intelligence (AI) would be far superior (72%), but they have reservations. This final installment on the perceptions and realities of using AI in financial institutions reveals an interesting trend of perceived barriers: cost, complexity and transparency.

The firms don’t feel they understand the technology enough to gauge its effectiveness, and that includes the specialists charged with administering and evaluating FIs’ anti-fraud programs. Our research found that 60 percent of banks’ fraud specialists who use AI systems believe the technology is not transparent enough, and the same proportion view it as complicated and time-consuming.

Limitations can be overcome by adding Smart Agents, a powerful, distributed file system specifically designed to store knowledge and behaviors. This distributed architecture allows lightning speed response times (below 1 millisecond) and allows for unlimited scalability and resilience to disruption as it has no single point of failure.

FIs are keenly aware that the learning systems currently used to combat fraud are not up to the job. They express great interest in Smart Agents—AI-based systems that make real-time observations about interactions with human users— as the solutions would “know” account holders’ normal financial behaviors and could quickly spot unusual activity. More than two-thirds of surveyed fraud specialists view Smart Agents as ways to reduce manual review, a key priority for FIs in implementing learning system innovations. This may serve as a signpost for the way ahead: Specific applications like Smart Agents can turn AI from an abstract concept to a very real tool FIs can put to work for them.

The following are some of the key findings from our research:

  • FIs see AI’s potential to more effectively fight fraud, but most don’t use it
    • Just 5.5 percent of our sample banks employ “true” AI systems that can process and learn from large data sets and take personalized, case-specific actions
    • Among those that deploy AI, only 45.5 percent use it as part of their fraud prevention efforts
  • FIs believe AI’s real benefit is that it reduces manual review and exception processes
    • 2 percent of FIs’ fraud specialists see reducing the need for manual review as a chief learning system benefit
  • Surveyed FIs have concerns about AI’s complexity and transparency
    • 60 percent of their fraud specialists feel the technology is not transparent enough, complicated and time-consuming
    • The same specialists are more likely to fault data mining for its lack of adaptability (56.8 percent) and limited real-time functionality (48.6 percent)
  • FIs express strong interest in using AI’s dynamic capabilities to improve fraud prevention
    • 90 percent of respondents involved in fraud detection and analysis are at least “somewhat” interested in Smart Agents
    • 7 percent believe the solution would help reduce manual review
    • 9 percent believe Smart Agents would reduce payments fraud

Someone once said that the only thing in life that’s constant is change; that holds doubly true for bad actors in the fraud business. Only true AI with Smart Agents can stay one step ahead by constantly updating profiles and predicting fraud though anomaly identification. For FIs to stay ahead of the bad guys—and their competitors—adoption of true AI is key to consumer protection, profitability and growth.

Read the full report here to learn what else FIs told the analysts at PYMNTS.com, and what that means for fraud detection and prevention.

The post AI and Fraud: What Financial Institutions Told Us appeared first on Brighterion.

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As artificial intelligence (AI) and machine learning (ML) become more commonplace in the business world, financial institutions are realizing the role these technologies play in their sector. Use cases include preventing fraud, insider trading, and money laundering while identifying opportunities for better customer service and new markets.

As may have read previously on this blog, Brighterion collaborated with PYMNTS to analyze how AI and ML are being used by financial institutions (FIs) across the U.S. We surveyed 200 financial executives from banks and credit unions with assets ranging from $1 billion to over $100 billion, resulting in 12,000 collected data points for analysis.

We were surprised by some of the things they told us, while others confirmed what we suspected. For instance, in our joint report AI Innovation Playbook: how FIs are using AI and ML, we learned that larger institutions were further down the path of adoption than smaller ones. That made sense; they have more assets to track, are often more spread out geographically, and have bigger budgets to dedicate to IT and administration.

What surprised us, however, is the large number of FIs that have learning systems but don’t leverage the full potential. There is a clear opportunity to use technology more effectively.

The most used ML technology is data mining, with a 70.5 percent adoption rate. The problem is that data mining is not necessarily the most effective tool for the job. Data mining supports payment services well, but it’s only used for this purpose by 45 percent of FIs.

Large banks also reported they use fuzzy logic, business rules management systems (BRMS) and neural networks for data insight, while smaller banks tended to use one or two tools. As we discussed last time, BRMS is a very common form of machine learning, with 59.5 percent of banks reporting they used it for fraud prevention. Unfortunately, BRMS uses a predetermined set of rules, whereas fraudsters continually change their methods to stay ahead of their targets’ knowledge.

Few financial institutions (5.5%) leverage AI that uses both supervised and unsupervised learning. In essence, the platform analyzes data and builds and evolves profiles as more information is received, preventing fraud in real time as it easily identifies anomalies as they occur.

Smart Agents, developed by Brighterion’s data scientists, enable 10 or more machine learning technologies to interact and develop a true picture of each individual profile, using both supervised and unsupervised learning.

Smart Agents can understand the full behavior of any entity or individual. Once the data is received, Smart Agents enrich the data, saving our customers hours of tedious work and allowing them to focus on their businesses. They pull data from multiple sources, regardless of format, type, complexity or volume, providing unlimited scalability (see Aite Group’s report which names Brighterion #1 for scalability) and no disruption—no single point of failure.

Learning and making real-time observations from interactions with human users, Smart Agents apply this knowledge to create virtual representations of every entity with which they interact, building a digital profile that optimizes customer-facing payments and banking services. If there are 200 million cards in an ecosystem, there will be 200 million Smart Agents analyzing and personalizing their services to a degree that other ML systems cannot accomplish.

Yet very few FIs use Smart Agents, substituting a combination of ML tools for true AI systems. Decision makers need to understand and accept that ML offerings are not viable substitutes for true AI. ML simply cannot carry out certain functions without considerable human intervention and using it in these areas produces little benefit compared to what can be achieved with Smart Agents.

Low usage does not mean FIs — especially larger ones — are not interested in Smart Agents. Approximately 64 percent of those holding more than $100 billion in assets reported being “very” interested, with 9 percent “extremely” interested.

Some smaller banks also expressed interest in Smart Agents, with 13 percent saying they would consider adopting the technology.

Compliance, money laundering, credit delinquency and other forms of fraud happen in real time and are not predictable. Get the full report to learn more about the state of AI in banking.

The post 64% of large banks want supervised and unsupervised AI for fraud prevention appeared first on Brighterion.

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Artificial intelligence (AI) and machine learning (ML) make the banks that use them highly competitive. They’ve invested in advanced learning systems to automate and streamline their operations and give them a competitive edge.

You may have read previously on this blog that Brighterion collaborated with PYMNTS.com to analyze how AI and ML are being used by financial institutions (FIs) across the U.S.  We surveyed 200 financial executives from banks and credit unions with assets ranging from $1 billion to over $100 billion to get a feel for the industry as a whole. In a series of reports, we’ve presented analysis of the 12,000 collected data points. Today we’re highlighting findings from the first report, The AI Gap: Perception Versus Reality in Payments and Banking Services.

We learned the six most common machine learning tools used by financial institutions:

  • Business rules management systems (70.5%): enable companies to define, deploy, maintain and monitor information based on a predetermined set of criteria
  • Data mining statistical methods (59.5%): extract trends and other relationships from large databases
  • Case-based reasoning (32.0%): an algorithmic approach that uses the outcomes from past experiences as input to solve new problems
  • Fuzzy logic (14.5%): traditional logic typically categorizes information into binary patterns like black/white, yes/no or true/false; fuzzy logic presents a middle ground where statements can be partially true and partially false, accounting for much of humans’ day-to-day reasoning
  • Deep learning/neural networks (8.5%): technology loosely inspired by the structure of the brain, with a set of algorithms that use a neural network as their underlying architecture
  • AI system with intelligent agents (5.5%): personalize, self-learn and adapt to new information

The top four use cases for learning systems were supporting banking services (79.1 percent), enhancing payments services, (53.7 percent), customer life cycle management (46.2 percent) and credit underwriting (42.5). Banks also reported using machine learning for compliance and regulation, preventing internal fraud, merchant services, collections and supplier onboarding.

These findings also revealed a gap: financial institutions aren’t accessing true AI with these tools. While the above adoption rates may seem high, most are using inefficient systems.

FIs have invested billions of dollars in machine learning systems that are largely manual and repetitive, often using outdated rules to flag violations of anti-money laundering (AML) regulations. These systems have been largely ineffective at curtailing money laundering and, as a result, regulators in the United States and the European Union have issued more than $340 billion in fines for non-AML compliance since 2009. Additional costs include consultant fees and armies of back office agents.

Brighterion developed a solution to this problem, called Smart Agents . Learning and making real-time observations from interactions with human users, Smart Agents apply this knowledge to create virtual representations of every entity with which they interact, building a digital profile that optimizes customer-facing payments and banking services. This allows FIs to offer personalized financial and payment services. If there are 200 million cards in an ecosystem, there will be 200 million Smart Agents analyzing and personalizing their services to a degree that other ML systems cannot accomplish.

Financial institutions are uncertain about specific aspects of AI and ML technology, and yet an overwhelming majority of them have invested in it and are planning on investing more in the future. Regardless of what they have adopted and whether banks are as AI-capable as they say they are, it is undeniable that they are satisfied with their investments in these systems. We also heard that most of those surveyed felt they already had AI and, once informed, were largely interested in learning more about it.

Is your financial institution invested in AI? Are you getting the full benefit of what’s available? Get the full report to learn more.

The post Only 5.5% of financial institutions surveyed use effective fraud prevention AI appeared first on Brighterion.

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Last week, another notable AI milestone occurred. Fresh off the heels of the U.S. Treasury’s Financial Crimes Enforcement Network and Federal Banking Agencies’ recent initiative that urged financial services organizations to implement “innovative approaches” like AI to better combat money laundering, terrorist financing and other illicit financial threats, U.S. Senators Martin Heinrich (D-N.M.) and Rob Portman (R-Ohio) announced the Artificial Intelligence Initiative Act.

The bipartisan legislation represents a coordinated, national strategy for developing AI and will provide a $2.2 billion federal investment over five years to “build an AI-ready workforce, accelerating the responsible delivery of AI applications for government agencies, academia and the private sector over the next 10 years.” More specifically, the bill plans to:

  • Establish a National AI Coordination Office, an AI Interagency Committee across federal departments, and an AI Advisory Committee comprised of non-governmental experts to develop a National Strategic Plan for AI research and development and facilitate coordination across government agencies.
  • Require the National Institute of Standards and Technologies (NIST) to identify metrics to establish standards for evaluating AI algorithms and their effectiveness, as well as the quality of training data sets.
  • Require the National Science Foundation (NSF) to establish educational goals for addressing algorithm accountability, explainability, data bias, privacy as well as societal and ethical implications of AI. NSF will also fund research on both the technical and educational aspects of AI and its effect on society by awarding to up to five new “Multidisciplinary Centers for Artificial Intelligence Research and Education.”
  • Require the Department of Energy (DOE) to create an AI research program and build state-of-the-art computing facilities that will be made available first and foremost to government and academic AI researchers, but also to private sector users on a cost-recovery basis as practicable.

Many businesses are spending exorbitant amounts of time and money on what they think is AI, yet in reality failing to reap any of the benefits of true unsupervised learning technology. This legislation is desperately needed. If passed, here’s hoping the bill helps ensure the U.S. remains a long-term leader in AI, and even more importantly, that it drives better education and coordination of our country’s research and development efforts on truly autonomous AI systems.
To read a full version of the bill, click here.

The post This Just In: U.S. Senators Introduce New Legislation to Coordinate and Fund AI Development Over the Next Decade appeared first on Brighterion.

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Only 5.5% of financial institutions surveyed use effective fraud prevention AI.

Artificial intelligence (AI) and machine learning (ML) make the banks that use them highly competitive. They’ve invested in advanced learning systems to automate and streamline their operations and give them a competitive edge.

You may have read previously on this blog that Brighterion collaborated with PYMNTS.com to analyze how AI and ML are being used by financial institutions (FIs) across the U.S. We surveyed 200 financial executives from banks and credit unions with assets ranging from $1 billion to over $100 billion to get a feel for the industry as a whole. In a series of reports, we’ve presented analysis of the 12,000 collected data points. Today we’re highlighting findings from the first report, The AI Gap: Perception Versus Reality in Payments and Banking Services.

We learned the six most common machine learning tools used by financial institutions:

  • Business rules management systems (70.5%): enable companies to define, deploy, maintain and monitor information based on a predetermined set of criteria
  • Data mining statistical methods (59.5%): extract trends and other relationships from large databases
  • Case-based reasoning (32.0%): an algorithmic approach that uses the outcomes from past experiences as input to solve new problems
  • Fuzzy logic (14.5%): traditional logic typically categorizes information into binary patterns like black/white, yes/no or true/false; fuzzy logic presents a middle ground where statements can be partially true and partially false, accounting for much of humans’ day-to-day reasoning
  • Deep learning/neural networks (8.5%): technology loosely inspired by the structure of the brain, with a set of algorithms that use a neural network as their underlying architecture
  • AI system with intelligent agents (5.5%): personalize, self-learn and adapt to new information

The top four use cases for learning systems were supporting banking services (79.1 percent), enhancing payments services, (53.7 percent), customer life cycle management (46.2 percent) and credit underwriting (42.5). Banks also reported using machine learning for compliance and regulation, preventing internal fraud, merchant services, collections and supplier onboarding.

These findings also revealed a gap: financial institutions aren’t accessing true AI with these tools. While the above adoption rates may seem high, most are using inefficient systems.

FIs have invested billions of dollars in machine learning systems that are largely manual and repetitive, often using outdated rules to flag violations of anti-money laundering (AML) regulations. These systems have been largely ineffective at curtailing money laundering and, as a result, regulators in the United States and the European Union have issued more than $340 billion in fines for non-AML compliance since 2009. Additional costs include consultant fees and armies of back office agents.

Brighterion developed a solution to this problem, called Smart Agents . Learning and making real-time observations from interactions with human users, Smart Agents apply this knowledge to create virtual representations of every entity with which they interact, building a digital profile that optimizes customer-facing payments and banking services. This allows FIs to offer personalized financial and payment services. If there are 200 million cards in an ecosystem, there will be 200 million Smart Agents analyzing and personalizing their services to a degree that other ML systems cannot accomplish.

Financial institutions are uncertain about specific aspects of AI and ML technology, and yet an overwhelming majority of them have invested in it and are planning on investing more in the future. Regardless of what they have adopted and whether banks are as AI-capable as they say they are, it is undeniable that they are satisfied with their investments in these systems. We also heard that most of those surveyed felt they already had AI and, once informed, were largely interested in learning more about it.

Is your financial institution invested in AI? Are you getting the full benefit of what’s available? Get the full report to learn more.

The post How many financial institutions effectively use AI to prevent fraud? appeared first on Brighterion.

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Brighterion recently surveyed more than 200 financial institutions across the U.S. with assets ranging from $1 billion to more than $100 billion to determine how AI and machine learning are being used in the financial services industry. According to our findings, adoption of AI and machine learning is admirably high, with many financial organizations working to implement the technology to more effectively service their customers, manage new and ongoing investments, combat fraud and augment their workforce. However, our data also indicates that few financial organizations are successfully leveraging AI to the full extent of the technology’s power.

Too often, financial organizations fail to recognize the stark differences between various supervised and unsupervised learning technologies, and many neglect to consider which AI and machine learning functions are best suited to specific business objectives. To realize the full potential of AI and make the most of such a substantial technological investment, financial organizations need to resist getting swept up in the collective AI hype and instead focus on the technology’s most fundamental capabilities.

Check out Dr. Akli Adjaoute’s recent piece in Banking Exchange to learn more about what true AI technology includes and how financial organizations of all sizes can realistically incorporate AI and machine learning to power large-scale, hyper-personalization.

The post The AI disconnect in the financial services industry appeared first on Brighterion.

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Last month, we had the honor of being named “Most Scalable Platform” by Aite Group in its latest report, “AIM Evaluation: Fraud and AML Machine Learning Platform Vendors” on the overall competitive position of fraud and anti-money laundering (AML) machine learning platform technology vendors. We were also named a Leader of the Contenders.

Unprecedented Scalability Sets Brighterion Apart

According to Aite Group, our strong showing was in part due to the Brighterion platform’s unprecedented scalability, which is more than twice that of our closest competitor, with 62,000 transactions per second (TPS) in production. This is a key differentiator in a vital industry that we are extremely proud of achieving, as it demonstrates how effectively and seamlessly we’re able to provide streaming infrastructure with no underlying databases to a variety of customers across the globe.

As a pioneering leader in providing intelligent decisioning around AI and AML, we’ve watched other industry players jockey for a foothold in this highly competitive and rapidly emerging space. Aite Group recognizing Brighterion’s early leadership efforts, customer resonance and our platform’s ability to bring true AI innovation to combat fraud and money laundering, is testament to our commitment and demonstrable successes to date.

Brighterion Also Recognized for Seamless Customization and Fast time to Value

According to Julie Conroy, Research Director of Aite Group, “We were impressed by Brighterion’s patented modeling approach which applies a combination of 10 machine learning techniques to understand behavior and flag anomalies. Brighterion stands out in terms of strong customer feedback, its platform’s fast response time and its production-ready models that can continue to iteratively learn.”

Building on our industry momentum, we’re excited to continue helping our customers customize Brighterion technology to fit their unique needs and ensure seamless execution via our Smart Agents and AI Express products. By working with customer organizations directly to collect the most relevant data, collaborate on specific outcomes and develop AI models in less than two months, we’re providing the fastest time to value in our industry, not to mention optimal ROI.

To access Aite Group’s proprietary report on fraud and AML machine learning platform vendors, please visit: https://brighterion.com/aite-report/. All rankings are based on detailed requests for information, product demos and interviews with client references.

The post Brighterion Named Most Scalable Platform and Leader of the Contenders in Latest Aite Group Report appeared first on Brighterion.

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2017 has been quite a year! We are proud to be the inaugural Morgan Stanley Fintech Company of the Year, and be named as a 2017 Cool Vendor by leading analyst firm Gartner, Inc.,

The Fintech Company of the Year award recognizes companies that make the most significant impacts on Morgan Stanley’s mission to continuously innovate for their customers.

“Brighterion adds a new layer of protection to help keep our clients’ assets safe,” said Jason Dandridge, Managing Director and Global Head of Fraud at Morgan Stanley, and credited the open architecture that gives his teams a “faster and easier way to build, enhance and adapt models.”

Akli Adjaoute, founder, president and CEO, highlights how Brighterion’s artificial intelligence and real-time transaction analysis prevents fraud and money laundering.

Read Morgan Stanley’s news release.

When Gartner creates its annual list of Cool Vendor, its analysts are looking for small companies offering technology that is:

  • Innovative — allows users to do things previously not possible.
  • Impactful — has or will have a direct impact on business, not just technology for its own sake.
  • Intriguing — has attracted Gartner’s curiosity or interest during the previous year.

“Gartner’s 2017 Cool Vendors reflect the need to stand out from the crowd, the ability to keep pace in fast-changing digital scenarios and the aptitude to solve problems that have persisted over many years,” said Daryl Plummer, vice president and Gartner Fellow. “Being a Cool Vendor in 2017 is all about both standing out and fitting in. In many cases, the Cool Vendors that stand out do so because they offer a major disruptive capability or opportunity.”

Gartner states CIOs and IT leaders should partner with digital disruptors if they want to maintain a pace of high, sustainable innovation.

Read Gartner’s news release.

The post 2017 closes out with two prestigious awards appeared first on Brighterion.

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