Use Case 2 - Enhanced Reporting Capabilities

The Problem
At this point, most institutions have implemented large scale enterprise data warehouses which they use as a central archive for bank data. These data warehouses have come with multi-million dollar investments to implement and are also expensive to maintain. Additionally, these environments rely on rigid data structures which becomes a blocker in being able to be nimble and responsive to analytics needs. Finally, these environments make it more expensive to bring in new data sources and integrate them with existing data.

The Big Data Solution
The Big Data approach is fundamentally different than the data warehouse approach. Rather than spending millions of dollars in extracting, transforming, and loading data into a predetermined schema, the best practice is to extract data in its native format and load the data into the “Data Lake” which resides in the Big Data environment. This approach substantially reduces the cost and time that it takes to bring in and combine large data sets into a common environment. With this paradigm, organizations are no longer penalized (by high price tags and long lead times) for bringing in “all of the data” and therefore do not need be overly careful about which data they bring over and which data they leave behind. Additionally, storing data in the Big Data environment is relatively less expensive than traditional data stores as Big Data typically runs on more commodity hardware.

Once the data resides in the Data Lake, data scientists can execute any transformations that they require at the time that they go to query the data. This “query on read” approach provides much more flexibility to run reports, analytics, and gain insights from your AML data. Whereas with the traditional data storage and “query on write” approach assumes that you know all of the questions that you will ever want to ask of your data, the “query on read” / Data Lake approach assumes that you will always have new questions and provides a platform on which you can quickly create question focused datasets.

Let’s consider a practical example: Regulators usually ask some standard questions which can be answered by traditional reports that you have generated. Sometimes however they ask you a very specific question which is not satisfied by one of your current reports. Let’s analyze how this scenario would play out in with the traditional approach to data management vs. the Big Data / Data Lake approach.

Traditional Approach: As you analyze the data that you have available to you in your standardized data table, you realize that you are missing a key field that you had not previously thought you would have needed from the source system. In this case you put in a request to your technology team which then has to go back to the source systems, identify the corresponding fields, write new extraction scripts to bring the new data into your data warehouse, write transform scripts to get it into the proper format, and then write new queries to support the regulator’s question. While your institution may have streamlined processes and procedures of handling these scenarios, often they result in long delays, increased costs, and a lot of unnecessary paperwork.

Big Data Approach: In this approach, you would determine the data fields that will be required to answer the regulator’s question and then go to your data lake to locate the corresponding fields. Once you identify these fields, you will write a query which retrieves the data and transforms it into the format that you need in order to answer the regulator’s question. As you can see from this example, the value in the Data Lake approach is that you always have access to the data that you might need without having to return to the source.

Use Case 3 - Platform for Data Exploration and Intelligence Analysis

The Problem
Combating financial crime oftentimes involves searching for patterns, trends, or anomalies across millions of customers and/or billions of transactions. The questions that you are seeking answers for across this data set change frequently. This week you may be looking for signals of human trafficking among your customers and next week you may be looking to find customers who are truly money service businesses. Not only do these questions change frequently, but oftentimes the questions and answers are complex and if limited to traditional data exploration tools, relational databases, and structured data, these answers may be impossible to find.

The Big Data Solution
The goal of implementing a Big Data platform is to provide flexibility and robust tools whereupon AML teams can openly explore the data to find the answers that they are looking for. Big Data not only allows AML programs to aggregate large volumes of data from a large variety of data sources, but also provides a large ecosystem of tools which can be leveraged based on the question that you are trying to answer. Things that are very difficult (if not impossible) to do using traditional relational databases and tools such as natural language processing, similarity clustering across massive data sets, and machine learning techniques provide your AML analytics team with a new toolbox for solving these problems.

A few examples of the type of data exploration that you may be enabled to do using Big Data include:

• SAR/STR Clustering- generate clusters based on attributes within STR/SAR filings to determine common attribute and unexpected relationships
• Peer Group Clustering- Cluster large volumes of businesses based on peer groups to identify anomalies to expected behavior. For example, find a business which is supposed to be a gas station which is clustering closer to a MSB.
• Negative News Analysis- Apply NLP and sentiment analysis to AML negative news feeds to identify common AML trends and risks as the emerge.

Use Case 4 - Large Scale Processing of New & Unstructured Data Sources

The Problem
Much of Financial Institutions are looking for when it comes to AML risks cannot be found in conventional If/Then statements running against transaction data. Many times what we want to know is whether or not one of our customers is a subject to some kind of negative news. Other times we may be wanting to understand a trend in the narratives that our AML analyst teams are writing as an output of their investigations; perhaps there is a trend of MSBs disguising themselves as retail stores or marijuana dispensaries disguising themselves as pharmacies. These questions cannot be answered strictly by looking at the structured data that we have in our transaction monitoring tools and therefore we ask humans to collect the data and analyze for these trends.

The Big Data Solution
One of the largest benefits of using Big Data technology is the capability to process unstructured data. With Big Data tools, the machine can store, process, and extract intelligence from large blocks of text. This is ideal for AML as we can use these tools to:

• Process negative news- Archive negative news feeds from common AML data vendors and process this data looking for names, business names, keywords, trends, and sentiment. Let the machine take the first pass at identifying if a negative news article is truly aligned to your customer. Look for trends in negative news; for example, see that there is an increase in wire fraud out of Eastern Asia among your customers.
• Process Case File Narratives- Extract intelligence and trends from the narratives that your AML analyst team is generating. Find common AML risks that your AML analysts are reporting.
• Extract intelligence from SARs/STRs- Extract intelligence and trends from the SARs that your team is filling out. Are there common themes among certain types of customers and specific AML risks?

Use Case 5 - Next Generation Case Management

The Problem
Case Management tools are at the center of every AML program and serve as a system of record for all of the AML investigations including transaction monitoring, high risk customer, and watchlist management investigations. Unfortunately, most case management systems fail to empower AML programs with added efficiency but rather become cumbersome systems that analysts have to work around in order to do their job. Most analysts find ways of minimizing the time that they spend in their case management system. They open a case assigned to them in the system, quickly toggle out of the application to conduct their investigation, and then return to the system at the very end to upload the output of their investigation and mark the investigation complete. As institutions continue to load their systems with more and more records and data, the systems slow down and become even more unusable.

The Big Data Solution
The next generation of case management solutions, based on big data technologies, will unlock automation and efficiency in AML investigations. These next-generation systems will take advantage of Big Data and other contemporary technologies to:

• Aggregate investigation data- Rather than logging into a dozen different applications outside of the case management tool hunting down the transaction, alert, and customer data that an analyst needs for an investigation, next generation systems will present all of the relevant data for a case in a single interface for the analyst. This aggregation is possible due to the Big Data environment’s strength in handling Variety in data formats. Using traditional relational databases, it can be very expensive and difficult to aggregate data because it requires transforming the data into a singular canonical schema. This can be near impossible when the source data systems are drastically different such as in the case of Wire Transfer transaction data vs. checking account transactions. This problem is more easily address in the Big Data environment as you are not required to execute these transformations ahead of time.
• Link/Network Analysis- Another value proposition of Big Data Case Management tools is the capability of leveraging Big Data graph capabilities for Link/Network analysis. Many tools exist in the Big Data ecosystem which can enable mapping of nth degree network connections and providing an interface to the analysts which allows them to shuffle through these network graphs to find risky connections.
• Entity Resolution / Single View of the Customer- One of the most persistent challenges faced by AML programs is drilling down to a single view of their customers. The complexity of institutions who have retail, wealth management, capital market, and other siloed lines of business make it very difficult for AML programs to understand the comprehensive relationship of a single customer with the institution. This is of course even more difficult for institutions with a global footprint. Beyond just bringing the data together, banks must have a way of merging customers together who are probably the same individual. Can you imagine how many “John Smiths” there may be at a financial institution with tens/hundreds of millions of customers? The Big Data environment provides an ecosystem and analytic technologies which allow data scientists to construct more advanced matching algorithms. Using a larger set of matching attributes allows us to snap together to similar entities with greater confidence. By incorporating these matching algorithms into a case management tool, institutions can merge probable matches and provide an interface in which analysts can review the probably matches and affirm or deny the match.