Money laundering is a global problem that affects the integrity and stability of the financial system. According to the United Nations, $800 bln, or almost 5% of the global GDP is laundered annually. To combat this threat, financial institutions and other regulated entities need to comply with the legal requirements to prevent, detect, and report money laundering activities.
This is where anti-money laundering (AML) solutions come in. They help businesses protect their reputation, avoid fines and penalties, and combat financial crime. The global AML software market stood at $1.63 bln in 2021 and is expected to reach $7.64 bln by 2032. In this article, we will explain the key features of AML software and how to develop it step by step.
AML Solutions: What Are They?
Anti-money laundering software is aimed to help financial institutions and other regulated entities comply with the legal requirements to prevent, detect, and report money laundering activities. Money laundering can facilitate various crimes, such as terrorism, drug trafficking, tax evasion, and fraud, and pose serious threats to the integrity and stability of the global financial system. Just imagine that finance companies filed more than 6k suspicious activity reports (SARs) in 2020!
AML software can help businesses implement effective AML compliance programs, which are essential to avoid fines, penalties, reputational damage, and legal consequences. AML compliance programs usually consist of four main components: customer due diligence (CDD), transaction monitoring, suspicious activity reporting, and record keeping. AML solutions can automate and streamline these processes, as well as provide analytical tools, risk assessment, and audit capabilities. Let’s look at their key features.
Key Features of AML Software
AML solutions can vary in terms of functionality, complexity, and customization, depending on the needs and preferences of different businesses and industries. However, some of the common features that most AML products offer are:
Customer Due Diligence (CDD)
This feature allows you to verify the identity and background of your customers, as well as assess your risk profile and source of funds. CDD can include identity verification, document verification, biometric verification, watchlist screening, and enhanced due diligence (EDD) for high-risk customers. AML software can automate the CDD process by using various data sources, such as government databases, credit bureaus, and third-party providers, and applying machine learning and artificial intelligence techniques to validate and enrich the customer data. Deutsche Bank had to pay $150 mln of penalty in 2020 because it failed to maintain proper CDD controls.
You’ll be able to monitor and analyze the transactions of your customers, as well as detect and flag any unusual or suspicious patterns or behaviors. The global transaction monitoring market stood at $8.4 bln in 2022 and is expected to reach $9.01 bln in 2023. Moreover, the market size is predicted to grow to $34 bln in 2033.
Transaction monitoring can include rules-based, risk-based, and behavior-based approaches, as well as anomaly detection, network analysis, and scenario modeling. AML solutions will automate the transaction monitoring process by using advanced analytics and machine learning algorithms to identify and score the transactions based on various risk indicators, such as amount, frequency, location, beneficiary, and purpose.
Suspicious Activity Reporting (SAR)
This feature allows you to report any suspicious or potentially illegal transactions or activities to the relevant authorities, such as the Financial Crimes Enforcement Network (FinCEN) in the United States, or the Financial Intelligence Unit (FIU) in other jurisdictions. By the way, businesses filed 3.6 million SARs to FinCEN in 2022.
SAR can include alert management, case management, investigation management, and reporting management. AML software can streamline the SAR process by generating and filing the reports in the required format and frequency, as well as providing audit trails and evidence for each report.
You’ll be able to store and maintain all the records and documents related to your customers, transactions, and reports, as well as comply with the retention and disposal policies. AML compliance requires businesses to spend $10.1 mln per year. About 30% of this amount is spent on the record-keeping process.
Record keeping can include data encryption, data backup, data access, data deletion, and data archiving. AML solutions can automate the record-keeping process by using secure and scalable cloud-based or on-premise storage solutions, as well as providing data governance and data quality tools.
How to Develop AML Software
Creating AML solutions can be a challenging and complex task, as it involves various technical, legal, and business aspects. The Elinext experts have made a short guide for you:
Step 1. Define the Scope and Requirements
The first step is to define the scope and requirements of your AML software project, such as the target market, the target customers, the target regulations, the target features, and the target budget. You need to conduct market research, competitor analysis, customer analysis, regulatory analysis, and feasibility analysis, as well as gather and prioritize the user stories and functional and non-functional requirements.
Step 2. Design the Architecture and User Interface
The second step is to design the architecture and user interface of your solution, such as the data sources, the data models, the data flows, the data processing, the data storage, the data security, the data visualization, the data interaction, and the data feedback. This stage can involve creating various data diagrams, schemas, pipelines, APIs, dashboards, reports, and mockups, as well as applying the best practices and standards for data engineering, data science, and data design.
Step 3. Develop the Code and Algorithms
The third step is to develop the code and algorithms of the AML software solution, such as the extraction, transformation, loading, validation, analysis, scoring, alerting, reporting, and optimization of data. You should choose a programming language, framework, library, and other tools, such as Python, R, Java, SQL, Spark, TensorFlow, Scikit-learn, Pandas, NumPy, Matplotlib, Seaborn, Plotly, Dash, Flask, Django, AWS, Azure, Google Cloud, and more. Apply the best practices and standards for software engineering, testing, and documentation as well.
Step 4. Deploy the Solution and Integrate the Systems
The fourth step is to deploy the solution and integrate the systems, such as the data sources, the data platforms, the data applications, the data services, the data clients, and the data regulators. You should use various deployment and integration methods, such as cloud-based, on-premise, hybrid, web-based, mobile-based, desktop-based, API-based, and more.
Step 5. Evaluate the Performance and Quality
The fifth step is to evaluate the performance and quality of your product, such as the accuracy, completeness, timeliness, relevance, reliability, usability, scalability, security, and compliance of data. You’ll need to choose performance and quality metrics, such as precision, recall, F1-score, ROC curve, AUC, accuracy, error rate, sensitivity, specificity, MAE, MSE, RMSE, R-squared, NPS, CSAT, retention rate, churn rate, and more.
Step 6. Collect the Feedback and Iterate the Process
The sixth and final step is to collect the feedback and iterate the process of your software, such as the data users, the data customers, the data stakeholders, the data regulators, and the data developers. Choose proper feedback and iteration methods, such as surveys, interviews, focus groups, reviews, ratings, comments, suggestions, complaints, bug reports, feature requests, and others.
According to Deloitte, 48% of financial institutions couldn’t follow the compliance regulations due to insufficient or outdated AML compliance technology. That’s why it’s important for development companies to offer banks up-to-date and convenient AML software. These solutions help the financial market participants to struggle with money laundering, stay compliant and mitigate risks.
Solutions created with the help of innovative technologies, such as artificial intelligence, machine learning, and distributed ledger technology, can enhance the accuracy, speed, and scalability of the AML processes. This type of software also faces some challenges and pitfalls, such as data quality, data security, data privacy, data compliance, and data integration, which need to be addressed and resolved.
You should be aware of the potential risks and benefits of AML solutions and adopt them in a responsible and ethical manner. The Elinext specialists are ready to answer all your questions regarding the development and maintenance of AML software and tell you about the recent trends in the industry.