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Identify Attacks on Financial Networks Quickly with AI and Data Visualization

The digitization of business transactions using new FinTech networks and software combined with the rapid spread of cashless settlements has increased threats to the security of operations for retail banks and other financial firms. Improving usability while maintaining privacy for consumers’ identities and other data is challenging, and organized criminal groups are using increasingly sophisticated tools and methods to conduct cyberattacks, steal identities and money, and compromise the overall security of financial systems. Banking networks are a prime focus for this type of theft. However, a range of advanced new techniques for identifying attacks quickly are being employed so corrective actions can be taken and prevent large losses. 

Flexible Detection Defenses for Sophisticated Attacks

In recent years the financial services industry globally has seen a sharp increase in cyberattacks and resulting costly data breaches. Today, a first line of defense is flagging suspicious transactions which include withdrawals and remittance through misuse of bank accounts and ATMs. These attacks are often more than simple thefts; the groups responsible also use these breaches to facilitate their money laundering activities. A common occurrence involves fraudulent emails instructing victims to transfer money using an ATM; the money is then transferred to another account and withdrawn at a bank branch. Impersonation cases are on the rise as well. 

Cashless settlement systems are another major target for attacks. Besides direct losses, these require notifications to multiple government agencies and investigations into the methods used in order to help prevent reoccurrence. They also expose FinTech firms and their associated financial institutions to substantial reputational risk.

Despite the investments financial firms have made in security systems, these crimes continue to multiply. Experience has also shown that conventional rule-based security systems are simply not advanced enough to completely deter or obviate such attacks.

In rule-based defense systems, conditions are set based on past attacks; transactions matching past conditions are flagged as suspicious. Such a simplistic “whack-a-mole” approach cannot deal with the new techniques that are continuously being developed and deployed by bad actors. 

Building Predictive AI Model Without Coding

Financial institutions must implement smarter, more flexible solutions leveraging artificial intelligence (AI) and real-time stream processing and visualization capabilties in order to respond to constantly evolving threats. Altair® Knowledge Studio® enables business users, fraud experts, and risk specialists to develop advanced AI models using an interactive graphical user interface that doesn’t require writing code, so users do not need to become experts in data science to effectively use of the software. Solutions leveraging AI are the most effectitive method to detect potential attacks since it helps firms deal with the “unknown unknowns” that criminals are constantly developing to breach their security.

AI algorithms may be broadly classified into “supervised” and “unsupervised” methods. It’s important to understand that supervised methods comprise many variations, including decision trees, neural networks, and ensemble learning. There is no universal algorithm that will predict every possible threat, and therefore firms must employ several different processes as needed. By using historical data with a trial-and-error approach, firms can discover which algorithms are likely to produce accurate results and begin using them quickly. With Knowledge Studio, individuals and teams can build extremely complex fraud detection systems using drag-and-drop programming, and if necessary, load in models developed by third parties in Python or R to amplify the efficacy of their models.

Knowledge Studio’s graphical point-and-click workflow and wizard-driven interface support a short learning curve, making it ideal for fast-changing fraud detection applications. Users simply select the type of model they wish to build, add new connectors and processes using pull down menus and/or wizards, and “draw” their workflows on the screen. With Knowledge Studio, users can focus their time on constructing models, evaluating results, and determining best responses to potential threats instead of learning complicated coding practices. 

Knowledge Studio’s interactive interface allows users of different skill sets to connect to a wide range of data sources, transform data stored in disparate formats into usable datasets, and generate insights with a huge variety of AI modeling techniques and algorithms, from decision trees to regression models to deep learning (neural networks).

Process and Visualize Real-Time Transactions

Altair Panopticon™ supports a complementary approach to detecting potential fraud. The software is built from the ground up to handle and visualize real-time streaming data from any number of sources. This makes it easy to spot the outliers, clusters, anomalies, and trends that may indicate fraudulent behavior. 

Like Knowledge Studio, Panopticon does not require any coding skills to use effectively. Business users can connect to virtually any streaming real-time data source and databases of historical data to build new analytical dashboards on-the-fly. Panopticon streams data with timestamps down to the nanosecond level, allows analysts to identify issues that would otherwise escape notice in the vast amounts of fast-changing transaction data they must monitor. Real-time analysis enables users to respond immediately to unusual and unexpected threats. 

This video demonstrates how to use visual analytics to monitor performance and profitability of a network of ATM machines. The system can monitor the number of transactions for each machine and identify machines that under-performing due to lack of supplies, paper, failed transactions, and/or funds. The dashboards also utilize real time data from the ATM network to help managers identify cases of potential fraud, including card cloning.

Combine AI and Real-Time Data Visualization to Build Advanced Attack Detection Systems

Financial firms can leverage multiple Altair AI and visual analytics capabilities to design the most effect solution to detect attacks. For example, a predictive model built in Knowledge Studio may flag possible attacks and real-time dashboards built with Panopticon can help verify that an attack is underway. A team unaided by analytics software would find itself quickly overwhelmed by the sheer volume of transactions being generated every hour. This combination of analyst teams supported by predictive AI models and streaming analytics reduces costs and improves response times to cybercrime detection. 

ATMs: A Major Target 

ATMs are often a target of criminal rings engaging in identify theft as well as monetary fraud. Here’s how Altair’s AI and data analytics solutions protect ATMs using combination of AI and real-time data visualization. 

The patented decision tree models incorporated into Knowledge Studio are particularly useful in this scenario. Their intuitive operations and advanced functions enable them to be modified as needed to detect ever-changing attack profiles. Analysts can use historical data of previous attacks to train the models to look for new variants on old patterns of withdrawal requests, amounts withdrawn, usage frequency, locations of withdrawals, and more. Panopticon can then display suspected transactions based on the predictive model running in Knowledge Studio.

The solution’s dashboard includes multiple charts enabling analysts to identify anomalies in real-time transaction data at a glance, for example by displaying normal transactions in green and potentially fraudulent transactions in red. Showing aggregated alerts, amounts, locations, usage frequencies by customer, and other data enables users to zoom in on the timeline of activity for a particular customer, ATM unit, or groups of customers and units as needed in order to determine whether a suspicious pattern requires action or not. They can change perspectives and hierarchies, and filter the data to isolate anomalies and reveal causal links that may indicate fraud or other irregularities.

Low-Cost Implementation with Altair Units Licensing

Panopticon and Knowledge Studio are available via the unique Altair Units licensing model. Altair pioneered a flexible, patented units-based subscription licensing model, which has accelerated the way customers use software by lowering barriers to adoption, creating broad engagement, encouraging users to work within the Altair ecosystem, and allowing for flexible and shared access to Altair’s offerings along with third-party partner products. Learn more here.

Are you ready to unleash the power of AI and data analytics to prevent financial fraud? Check out this “Guide to Using Data Analytics to Prevent Financial Fraud.”

To explore how Altair data analytics enables people of all skill levels can take full advantage of financial transaction data to identify trends, patterns, anomalies, and exceptions to more effectively combat fraud, contact our solutions experts today.

Additional Resources

Guide to Using Data Analytics to Prevent Financial Fraud

Financial Fraud Mitigation with Data Analytics

Arbor Financial Increases Efficiency with Accurate Reporting