In recent years, Artificial Intelligence (AI) has been at the forefront of metamorphic innovation AI has been keeping us at the cutting edge of progress in various areas of human endeavor. Saal has been at the forefront of this AI driven transformation and progress.
Artificial intelligence is a broad name given to technologies that are self-learning and adaptive. Machine Learning, Data-Science, Deep Learning and Advanced Analytics often are within the realm of AI.
AI includes a number of research areas like Natural Language Processing (NLP), Computer Vision, Digital Image Processing, Clustering, Classification, Speech Recognition, Expert Systems, etc. Many of these areas of AI have applications in the fields of cyber-security, fraud detection, fraud prevention, and financial crimes, particularly those related to Payments/Transactions and Money Laundering.
Criminals and hackers use increasingly sophisticated technologies to steal money and identity or to commit other types of financial crime. To uncover their new schemes and to handle increasingly sophisticated tactics, we need more than just standard analytics and rules to flag transactions. We require adaptive techniques that improve with time, machine learning to continuously learn about fraud and financial crimes and identify solutions to prevent them. Solutions that can help flag fraudulent transactions in real-time and can help perform investigations on the past transactions to detect instances of fraud. Solutions that can help ensure that our systems of payments, banking, and transactions are compliant to regulations (legal/regulatory requirements) and are secure from internal or external transgressions.
That’s where Supervised Machine Learning algorithms come in. These algorithms learn from historical data and identify patterns of interest that an investigator might want to flag. Labeled data – e.g., labeled historical transactions data, in which some of the transactions have been marked as fraudulent – needs to be provided as input to a supervised machine learning model (such as decision tree, random forest, neural network, gradient boosting machine or extreme gradient boosting, support vector machine, logistic regression, etc.; or an ensemble model combining more than one of these models), and the supervised machine learning model can be trained using the data (i.e., detect patterns in the data). The trained model can be used to identify fraud and the reasons for why a transaction is being labeled as a fraud (i.e., by answering what parameters indicate that a transaction is a fraudulent transaction; this can lead to identifying the reason and how the fraud was perpetuated).
Unsupervised Machine Learning algorithms can also help identify potential issues, anomalies, and patterns, and it can do this based on unlabeled data. So, if we do not have labeled data-set available, we can use an unsupervised learning algorithms (such as clustering, deep learning neural network, self-organizing map, or other; or a combination of these models made into an ensemble model) to identify issues, incidents, anomalies/outliers, and indications/patterns of fraud, which may not have been unearthed earlier (i.e. which may not have surfaced earlier), and which could represent previously unseen forms of fraud. These techniques detect behavior anomalies by identifying transactions that do not conform to the majority. These anomalies or discrepancies are evaluated at the individual level as well as through sophisticated peer group comparison, to achieve a higher accuracy of identification, e.g. identification of fraudulent transactions.
Network Analysis (using Graphs, Knowledge Graphs, and Graph Theory) can be used to identify paths, flows, connections and hubs that reveal unusual patterns. Analysis of the flow of funds can help in mitigating fraudulent transactions. There have been examples where terrorist activity has been detected by analyzing the flow of money across interconnected banking networks.
Text analytics (using Natural Language Processing, Semantic Analysis, Sentiment Analysis, Intent Analysis, Machine Translation, etc.) can be used to analyze text to accurately identify expressions of names, times, companies, monetary values and more, through search, content categorization, and entity extraction.
Combining these techniques together has proven to be far more accurate and effective than approaches based on rules or simple analytics. One can launch a large number of iterations of strategies to arrive at a model with exceptional predictive power. Since rules are still essential, machine learning can mine the data to define precise detection rules and can keep them current. This improves the efficiency of fraud detection and makes it possible to detect and prevent fraud from occurring in real-time.
The requirement of real-time transaction monitoring is a baseline requirement for enterprises now. Not only for financial transactions but digital event data surrounding authentication, session, location, and device. A real-time transaction monitoring system can score transactions for multiple types of risk during one authorization, giving a holistic view of risk. This can include AML (Anti-Money-Laundering) checks. Since most of AML data is similar to fraud detection-related data, real-time AML checks can be performed along with fraud-related checks on a transaction. Cybersecurity is a key element of AML compliance. AML is a crucial area of regulatory compliance for enterprises.
No financial institution wants to be used, intentionally or unintentionally, as a channel for money laundering. So, they must verify the real identity of entities that do business with them and who receives their transactions. The risks and penalties for weak AML controls have soared in recent times. The baseline standards for KYC (Know Your Customer) processes have also increased in recent times. AI can help in KYC processes by:
1.Speeding up and fortifying the authentication processes that validate digital devices and the in-person applicant
2.Using robotic process automation (RPA) to automate searches and queries of third-party data
3.Making it easy to bring unstructured data – such as text, images, and video – into a richer entity profile
4.Supporting collation of new data elements from different sources, such as ownership percentages and controlling interests
This can allow the financial institutions to reduce the human time required to identify, classify and analyze trade documents – for example from 700 hours (for a customer) to a matter of minutes. This helps improve the process of due-diligence of customers (KYC process) before onboarding – by augmenting human efforts with AI.
AI can also be used to identify card fraud at the point of sale. Payment processors can create a customer behavior profile of their cardholders. This profile can be used to drive a risk score for all transactions in real time at the point of sale. As a result, fraud detection accuracy and precision can be enhanced, and card fraud instances can be curtailed (by even 50 to 70 percent), and false positives can be cut in half.
AI can also help reduce false positives in money laundering checks. The screening systems of financial enterprises (e.g. a bank) flag a large number of false positives each day (e.g. 1,000 cases per day, in a bank). These need to be manually checked to ensure that false/wrong charges (of money laundering) are not brought against a customer. This is an unproductive use of time and resources, that can be optimized using AI.
AI can also help improve the investigation of cases of fraud, financial crimes and security breaches. Using historical data from various data sources, and by applying Automation based on Machine Learning, investigators can be enabled and empowered to perform investigations more efficiently and effectively.
So, to summarize, there are new innovations in digital technologies that are evolving rapidly, and these technologies are being adopted more ubiquitously. In such a world, AI-powered cybersecurity and fraud detection/prevention systems become more and more important for us. AI is now making its way into our lives and has a major role to play in safeguarding our digital frontiers and ensuring our security.