Introduction to AI and machine learning reveals the groundbreaking potential of these technologies to revolutionize industries, from healthcare to finance, by automating complex tasks and providing unprecedented insights from vast data sets.
Artificial intelligence and machine learning (AI/ML) are the part of computer science that are correlated with each other. AI and ML technologies are considered as the most in demand and trending technologies that may be used to create intelligent systems and techniques which institutions may use in different compliance and risk management processes such as AML and KYC compliance and risk assessments.
Introduction to AI and Machine Learning
Artificial Intelligence (AI) is a bigger concept to create intelligent machines that may simulate the human thinking behaviour and capability, whereas, the ML is an application of AI that allows machines to learn from available data without being explicitly programmed.
AI is a field of computer science that makes a computer system mimic the human intelligence. AI means a human-made thinking capability.
The AI system is concerned with maximizing the chances of desired success or objective. AI technology may completely deal with structured, semi-structured, and unstructured data.
The AI system does not require to be pre-programmed by the institutions or compliance specialists, instead of that, they use algorithms which may work with their intelligence. It involves ML algorithms such as “reinforcement learning” algorithms and “deep learning neural networks”. Based on capabilities, AI may be classified into three different types as Weak AI, General AI, and Strong AI.
Machine learning (ML) is about extracting knowledge from the data.
ML is a subfield of AI, which may enable the machines to learn from available data or past experiences without being explicitly programmed.
In ML, machines are taught with data to perform a particular activity or task and provide an accurate outcome. ML is mainly concerned with accurate analysis of data patterns.
ML enables a system to make predictions or take decisions using historical data without being explicitly programmed. ML uses a massive amount of structured and semi-structured data so that a machine learning model may generate accurate results or give predictions based on that available data.
ML algorithms may learn by own using historical or available data. ML may work for specific domains to detect pictures of humans, it will only give results for human images, but if we provide new data like a horse image then it will become unresponsive.
ML techniques and capabilities may help improve the process of performing customer due diligence, data identification, verification, sanctions screening, etc which ensures regulatory compliance.
ML tools may extract data in real-time from different available data sources, data sets, data fields, files, linked websites, and data portals. Therefore, the use of ML helps institutions in improving the regulatory compliance process, especially the anti-money laundering, and countering the financing of terrorism (AML/CFT).
The use of ML in AML and KYC compliance processes enable timely revisit and update of compliance program, policies, customer profiles, screening data, negative lists, transaction scenarios, and monitoring process.
Machine learning helps in the identification and understanding of broader data and different data sets and enables extracting meaningful data for decision purposes. ML may understand the customer screening parameters based on available and linked data sources, and data sets. ML based on input parameters, performs name screening, sanction screening, negative list screening, and various other screenings to improve regulatory compliance.
The use of ML algorithms may solve issues related to generation of excessive false positives leading to excessive compliance cost, and reducing incidents of “missed true matches” for transactions investigations.
ML helps in using complex big data, with hundreds of millions of names and large comparison scenarios, and provide meaningful data outputs for compliance decisions. ML may scrutinize customer transactions based on their linked profile matching factors, such as full and exact name of customer, date of birth, nationality, jurisdiction, sources of income, purpose of account, beneficiary, beneficial owners, etc. Using AI and ML for KYC demand input of correct customer data, to perform deep and relevant screening, matching, and verification of customer transactions against their risk profiles.
Using ML may help compliance specialists in countering different screening challenges such as typos, incomplete data strings, use of nicknames, spelling differences, etc.
ML models may help detect changes in the client or customer behavior by analyzing their current activities, and transactions and require compliance specialists to perform detailed review of such customers where suspicious activities or transactions are identified.
The use of ML algorithms may help in avoiding inadequate name screening, and increases the efficiency of AML/KYC measures through the identification of true transaction matches.
The use of ML avoids performing inadequate name searches and screening which can be detrimental, resulting in fines, reputational losses, and loss of customers. The ML capabilities enable sanctioned individuals. The use of ML algorithms detects and lowers the risk of identifying a customer who is not a sanctioned individual.
Final Thoughts
Artificial intelligence (AI) and machine learning (ML) stand as transformative pillars in the realm of computer science, closely intertwined in their functionalities. AI, a broad concept, seeks to replicate human cognitive processes, whereas ML, its subset, is designed to enable machines to decipher patterns and knowledge from vast data reservoirs without explicit programming. Institutions are capitalizing on these technologies to augment compliance and risk management processes, especially in the realms of Anti-Money Laundering (AML) and Know Your Customer (KYC) protocols.
The efficiency of ML in analyzing, predicting, and screening vast data points significantly streamlines compliance processes. It not only aids in precise customer profiling but also curtails the risk of false positives, mitigating potential compliance costs. Furthermore, ML models continually evolve, detecting anomalous behaviors and enhancing the precision of AML/KYC measures. This evolution signifies a substantial shift in how institutions approach compliance, reducing errors that could lead to significant financial and reputational repercussions.
