Strengthen Your AML Program With Data Mining (I)
Earlier this week we hosted a webinar together with Symptai Consulting. Hosted by Business Analyst Rory Barrett, the ‘Strengthen Your AML Compliance Program with Data Mining’ webinar provided attendees with an overview of data mining and different techniques that organizations can apply to improve their compliance programs. In case you missed it, here is part 1 of the webinar recap.
What is data mining?
Data mining is a fairly new concept as far as how to use information to help us make decisions. In this case the information we’re relying on is data that we already have within our own organizations. Usually when trying to use data to solve a problem, the data that we want tends to be disjointed; it can be in various places—for example, in customer relationship management (CRM), human resource management (HRM) and enterprise resource planning (ERP) systems, as well as in application software or Excel—and that can be challenging. Getting all of the information needed can be tedious and time consuming, and other cases of fraud could be happening while you’re trying to gather all of the information you need to conduct a proper investigation.
With data mining, you can take all of the information from these various sources and combine them together in such a way that you not only have easy access to the information, but you’re able to quickly learn from it and gain new insights through the use of data models.
Today we’re experiencing a phenomenon that we’re going to call the “data explosion” problem. Over the last two decades, technology has advanced rapidly, allowing us to generate so much data that we don’t know what to do with it all. We’ve become experts in collecting data on anything and everything, but we haven’t excelled at doing anything with it—until now. Data mining allows us to finally put that data to good use by managing large datasets and analyzing them to gain new insights that we can use to better respond to threats, quickly identify new opportunities, and even gain a competitive edge. We are drowning in data but starved for insight, and this is the problem we are trying to solve with data mining.
Technology changes every day, and it becomes challenging to keep up. It’s difficult to protect ourselves from new types of fraud and other threats that technology has introduced and that are not only harder to detect but can instantly have huge impacts on an organization.
To understand how data mining can impact your organization, let’s look at an example. Imagine that you are a compliance officer and your company is under investigation. Law enforcement claims it is involved in money laundering activities, and that one of your customers is suspected of funding illegal weapons purchases through her account. This 72-year-old client is a retired nurse and has been a customer for 32 years—her profile does not at all match what you would expect someone accused of these crimes to look like. You have to perform due diligence and investigate the client’s activities against your different controls to see if you can find anything suspicious. Upon investigation, you find that this client has not met thresholds, is not on any watch lists, and has not triggered any alarms. What are you missing?
Start mining your data
Data mining can help in a situation like this, but how do you get started? There are five main steps to consider when introducing data mining to an organization:
1. Identify your business objectives
What are you trying to achieve? Some common objectives include using data analytics to improve risk management strategies; identifying new customer trends and patterns to aid in customer retention; or launching a campaign to continuously clean the data circulating in the organization. There are many different ways that data mining can help you, but not all of them have the same requirements. A clear objective will help in managing not only the resources you have, but in validating the value of the type of insights you’re looking to get.
2. Access your data sources
Data can range from large datasets and data tables stored in core business applications to a series of Excel spreadsheets that you keep on your desktop or on your department’s shared drive. You’ve identified the different sources, now if you find them you can set up different data streams. If you know that you will consistently need this information, then you should set it up so that you have a consistent feed of that data. That way you can have the data when you need it—access is no longer an issue.
Then you need to understand the data in front of you. Typically this is where a data scientist would spend the majority of their time: assessing the state of the data, how it relates to each other, how complete is it, how do you want to treat the different values or codes that you’re seeing? Data isn’t always clean, and it takes time and effort to understand what you’re seeing and the value you can get out of your data. If you find any gaps, this is the time to address them. You may find that you don’t actually have as much useable data as you thought.
3. Prepare the data
Once you’ve prepared the datasets, you can think about your data models. How do you decide which data model to use? There are many to choose from and all do different things, so your choice is important because you want to use a model that best suits your business objectives. You need to be knowledgeable about the different types of models and the effects that they have.
4. Create the model
Once you’ve chosen a model, you’re ready to build something you can actually test. Over time your business needs will change, so you need to ensure that the model you build can adapt with changes not just in the business but in the types of threats facing your organization.
5. Test and deploy
Your model will need to be tested and validated—it’s a rigorous but necessary step. This should be made into a continuous process to ensure that you’re getting the best results out of your model over a longer period of time.
Stay tuned to our blog for part 2 of this webinar recap, which will offer more insight into various types of data models and how they can be leveraged to improve your compliance program.
About Anu Sood
Anu Sood (LinkedIn | Twitter) is the Director Marketing at CaseWare RCM and is responsible for the company’s global marketing strategy. She has over 20 years of experience in product development, product management, product marketing, corporate communications, demand generation, content marketing and strategic marketing in high-tech industries.