Can off the shelf AI help tackle financial crime?

Key Takeaways

1. Standardized, off-the-shelf AI/machine learning products allow financial institutions to detect financial crime more efficiently and effectively, with much faster implementation and time to value compared to bespoke solutions.
2. Machine learning algorithms—supervised, unsupervised, semi-supervised, and reinforcement learning—are increasingly used to analyze complex financial data and detect patterns and anomalies that can indicate fraud.
3. Standardized AI solutions offer proven algorithms, features, and interpretability frameworks, reducing complexity, bottlenecks, and the time needed to respond to ever-changing regulations.
4. Bespoke solutions, while tailored, require longer development and refresh cycles, often leaving institutions exposed to risks for longer, whereas standardized systems can be updated and maintained more rapidly.
5. Industry leaders like SymphonyAI combine decades of expertise with state-of-the-art AI technology, offering comprehensive, continuously updated, and widely trusted anti-financial crime solutions that meet regulatory expectations.

Using AI/machine learning in standardized off the shelf product suites for anti-financial crime accelerates results

Financial institutions need to modernize to keep up with ever-changing anti-crime regulations and laws around the world. However, with financial institutions having so much sensitive data, adopting AI machine learning (ML) can be challenging, leading enterprise customers to request bespoke services fitted to their specific needs. In this blog, we will explore whether standardization in the form of off the shelf AI may present financial institutions with both better efficiencies and better effectiveness.

Why use AI and machine learning in anti-financial crime?

Financial institutions are adopting ML to improve the efficiency and effectiveness of financial crime detection. A field of study in AI, machine learning involves the development of algorithms that allow computers to find patterns in data. They can forecast or make judgments about the findings using computational models.

Financial institutions are unique in their requirements. Not only must regulations and laws be abided by in every country in which they operate, but these laws and regulations are ever-changing, with steep fines and sanctions for failing to comply. Such is the extensiveness and difficulty of staying ahead in compliance that products are now available that crawl websites for upcoming law and regulatory changes. They even alert customers before they happen. Finance is an increasingly complex industry, and it’s not going to get any simpler.

Moreover, there are many different sectors within finance – banking, real estate, insurance, etc. – as well as different subsets within these sectors – stock vs. mutual insurance, for example – which, alongside the extensiveness of their sensitive data and the desire for a competitive edge, explains the desire for AI and bespoke products. While these different industries have different needs, there are enough similarities that clear trends are emerging for which algorithms perform the best or close to the best in identifying financial crime patterns within data. The time it takes to implement bespoke products leaves financial institutions exposed to ever more security liabilities until the product is ready.

By adopting an off-the-shelf AI product with consistently performing, optimized algorithms for each use case ready to go, financial institutions will dramatically improve the time to value of AI machine learning. This is the time taken to realize the value of the product. They will also cover themselves from the continuously updated regulatory risks as quickly as possible.

Types of machine learning algorithms

Machine learning comprises a variety of different algorithms that analyze data sets, identifying patterns, correlations, and anomalies that humans may miss. As they do so, they learn and optimize operations, improving performance, and developing models over time. In this way, the outputs can be used to forecast trends, make data-driven predictions, and identify potential risks and opportunities.

The main types of machine learning algorithms are:

• Supervised learning –the algorithm is taught by using data that is already understood alongside the desired inputs and outputs. The algorithm works out how to achieve those results. It can make mistakes as it learns and will be corrected by the operator.
• Unsupervised learning –the algorithm is given data and works on its own. With no instructions, it analyzes inputs and discovers patterns and correlations, grouping and organizing the data according to its findings. Over time, decisions improve as it refines its processes.
• Semi-supervised learning – a combination of supervised and unsupervised learning. The algorithm uses labeled and unlabeled data. The algorithm learns how to label and assign unlabeled data, refining its methods over time.
• Reinforcement learning – the algorithm is given a rigid set of instructions and information (actions, parameters, outputs). Through trial and error, the algorithm uses the data according to its instructions, monitoring and evaluating results to discover the most optimal approach.

What is standardized machine learning software?

It is well-known which algorithms tend to work with a specific use case. For example, decision tree-based algorithms are suitable for sifting through money laundering alerts to identify which can be ignored, thereby reducing false positive alerts. For specific use cases, we can reduce the time to implement ML software by using standardized, off the shelf AI applications.

By standardized, we mean:

• Having a standard data interface that covers the requisite data for a specific use case
• Having a set of standard ML algorithms proven to work well for the use case rather than starting from scratch
• Having a set of standard features proven to work well for the use case rather than starting from scratch
• Built using open-source industry-standard Python packages such as SciKitLearn and PyTorch.
• Having standard exports to a user interface for full interpretability, explicability, and explainability of the outputs of your standardized model

All of this adds up to achieving results faster using standardized, off the shelf AI/machine learning software. Additionally, data scientists are freed up to focus on complex edge cases. This is where a standardized approach is not sufficient or incrementally improves the standard approach.

Financial institution requirements

Machine learning algorithms for financial institutions must cover everything that the sector deals with daily. This includes everything from behavior changes (an influx of high deposits or sudden high-value transfers out of the country) to account openings that bypass traditional sanctions screening technology (using double letters in a name rather than one, for example).

When meeting financial institution requirements, responsible AI and end-to-end production-ready pipelines need to be considered. This includes everything that goes into training a model (data ingestion, feature engineering, model reinforcement, and interpretability). Furthermore, it requires the loading of risky alerts into the front-end for investigators.

Because exact requirements vary so much between enterprises, production-ready data pipelines can vary considerably. This is because of dealing with different ML algorithms and differing use cases. For example, identifying changes in behavior requires temporal profiling, whereas anomaly detection might not. These variations can add to both the cost and time of implementing new ML software.

The question then becomes – is it worth it?

Why standardizing machine learning software is the way forward

Standardizing ML SaaS software for financial institutions offers many benefits over a bespoke approach. What is lost in acquiring a program specifically designed for one financial institution’s needs is more than made up for by that same institution being able to implement the standardized software far more quickly than any other approach.

Simply put, the time taken to implement the differences and create a bespoke offering isn’t worthwhile. Because a standardized offering can be put in place so much faster, results often far exceed the bespoke approach.

Training a standardized model

As the diagram below shows, training a standardized model doesn’t take long, so it’s possible to go live sooner, often many months ahead of when a bespoke solution could feasibly be put in place. Additionally, the model can receive more training during its lifetime, constantly becoming ever more intelligent as time goes on.

This is because financial institutions need to continuously refresh models with the latest data to ensure they don’t go stale. Updating a bespoke approach takes time and is dependent on when software developers and data scientists within a team can work on the project. If there are many bespoke solutions, the projects can quickly add up!

With a standardized, off the shelf approach, where the developers and data scientists are familiar with the software, the result is a largely automated approach that ensures few bottlenecks and much faster refresh rates.

Furthermore, institutions rely on teams of people to interpret the risks they are alerted to. Different algorithms often require different model interpretability approaches (a means of explaining the output of an ML algorithm).

Having a standardized detection algorithm means financial institutions can adopt a standard interpretability approach that works well, and investigators will be familiar with the approach.

One final benefit of a standardized solution is that the company creating the software can put more money and time into the software. Though this may seem to be of little benefit to a customer, this isn’t entirely true. The resulting software package, extensively tested by many customers rather than just one, will be a much more comprehensive and complete offering with fewer bugs, more consistent updates, and faster turnaround times for oft-requested features.

The SymphonyAI solution

SymphonyAI solutions are developed by subject matter experts and data scientists who have decades of hands-on experience within the financial sector. The software consists of the trusted technology of NetReveal – a global leader in anti-financial crime for more than twenty years – alongside supervised, semi-supervised, and unsupervised Sensa machine learning models and a comprehensive suite of out-of-the-box rules. This has led to exciting advancements such as AI overlays and agentic AI.

The models are self-training and successfully undertake surveillance across hundreds of data sets. The unsupervised models are uniquely placed to discover previously unknown and unimagined new ways of monitoring behavior and discovering criminal activity by combining state-of-the-art profiling with leading anomaly discovery techniques. This combines with supervised models that assess risk similarity between activity observed and feedback from analysts from previous alerts.

In essence, users of SymphonyAI software can enjoy the best of all worlds. Tried and tested financial technology, state-of-the-art AI innovation, and continuously developed software. This creates a world-leading package that can be deployed immediately.

Machine learning is rapidly becoming the standard expected by regulators. By using SymphonyAI, enterprises gain access to a system that a third of the world’s top 100 banks use.

Get in touch to learn more about SymphonyAI financial services.