AI-powered applications and solutions have become a major catalyst when it comes to compliance checks, particularly in NLP. The aim of this article is to go through NLP techniques and how you can apply them to your company’s compliance process to make it easier and more efficient.

We have all had to adapt to the impacts of Covid-19 causing more and more companies to realize the importance and impact of digitalization. One of the significant issues that compliance faced during the pandemic was the lack of accuracy and quality as people were working from home.

Let’s first quickly go over some definitions for the point of this article:

Natural Language Processing (NLP) is the ability of a computer/software/application to be able to detect and understand human language through speech and text, just the way we humans can.

Compliance checks are the process of focused reviews and analysis to check whether the controls put in place and the outputs produced to meet the security requirements that have been implemented into the security and risk treatment plans.

Why are compliance checks important?

Having the right compliance checks put in place can save a lot of companies time and money. If your company does not follow the correct protocols, they are at a high risk of fines and penalties.

Let’s take the banking industry for example. The banking industry uses the Know Your Customer (KYC) process to help with compliance checks. KYC is a set of standards that are designed to protect financial institutions against money laundering, fraud, and other acts of corruption such as terrorist financing. The KYC process is used to perform mandatory checks in identifying and verifying the client’s identity.

We’ve all seen the rise in cyberattacks and fraudsters, some may have even asked you to help out. Therefore, the fight to ensure that compliance checks are done correctly is more important than ever.

According to the National Audit Office in the UK, the Government’s £47bn emergency lending scheme abused many KYC regulations due to a lack of compliance checks such as identity checks. Due to this, it was estimated that around 11% of loans were fraudulent.

Current issues with traditional compliance management

Compliance management comes with its own difficulties:

• Environments: Depending on your environment, systems and infrastructure can lose their management between different teams. Once this happens, you naturally lose control and raise the liability of risks and vulnerabilities.
• Teams: The bigger your team, the bigger the infrastructure you use causing your environment to be bigger. This also causes a natural loss of control and management.
• Security: As mentioned prior, an increase in threats leads to a lack of security due to constant threats. Due to this, an organization will have to constantly make changes to compliance checks which is a highly manual process.

NLP: Intelligent Process Automation

AI-powered applications have proven to improve not only a company’s workflow but its overall accuracy and performance — helping them dodge fines and penalties.

The NLP technique that I will be speaking about in this article is Intelligent Process Automation (IPA). You may or may not have heard about IPA, but it is very similar to Robotic Process Automation (RPA).

You may also have not heard of RPA, so let me define it for you.

Robotic Process Automation (RPA) is a technique used to make companies’ lives easier to build, deploy, and manage software robots or artificial intelligence workers. RPA technology is used to emulate human actions, such as website scrapping, call center operations, and more.

RPA uses repetitive manual tasks and learned data to then go on to use bots or software robots to replace them with automated workflows. Although this process is good and has proven to work, IPA takes RPA to the next level.

So what does RPA have to do with IPA?

Intelligent Process Automation (IPA) is also known as hyper-automation, intelligent automation, or digital process automation. It combines RPA with process mining, OCR, analytics, and artificial intelligence (AI). The automation capabilities performance has proven to increase the value of a business and its competitive advantage. It has shown an enhanced work environment, increases in performance, quicker reaction times, increases in efficiency, improved customer experiences, and lower operational risks.

IPA can be utilized in Computer Vision, Cognitive automation, process mining, NLP, and Machine Learning. Adding cognitive technologies such as AI to the mix with RPA allows businesses to expand their automation process — benefiting them in a variety of areas.

For compliance processes, the most used tool to process documentation is OCR, Optical Character Recognition. OCR has the ability to recognize text within a digital image and translates it into machine-encoded text. This can be used in invoices, bank statements, etc.

Although this is a well-used process, it comes with its limitations in the world of AI technology, namely interpretability. Although OCR can turn data into machine-encoded text, does it understand what the text means? Unfortunately no. Not only can it not understand the text, but the text also has to be printed and not handwritten — heavily reducing the performance of the tool.

The process also requires human intervention in order to complete the conversion and it cannot handle complex data. This is a manual process bottleneck as 80% of data being circulated today is in an unstructured format.

When it comes to compliance checks, this is a major limitation. This is where I introduce Intelligent Document Processing.

Small teams can make a big difference, just ask GE Healthcare’s Vignesh Shetty. A team of three worked to create automated mobile x-ray machines with high level processing tools to help streamline the urgent care process.

Intelligent Document Processing

Intelligent Document Processing (IDP) has the ability to convert unstructured data into a structured format using NLP. it creates valuable information from the data which provides end-to-end automation to document-centric business processes.

How does this process work?

1. Collection of Data — this is the first step in the IDP process. Different forms of unstructured and semi-structured data will be collated such as emails, PDF files, scanned documents, excel files, etc.
2. Pre-Processing — This next step involves binarization, noise reduction, cropping, and deskewing of the data to present the data in a more understandable format
3. Classification — At this point, the data is a better format for the documents to be classified using supervised and unsupervised tools such as NLP and OCR.
4. Data Extraction — Data is then extracted from the documents using NLP, NL, Deep Learning, and OCR. There are various tools that you can use, for example, Google Vision can detect handwriting in images.
5. Data Validation — At this point, we want to check if the model created is effective and meaningful and has successfully extracted the correct data inputs for compliance.
6. Human-in-the-Loop Validation — The best way to validate your model is to get a human to validate it.
7. Integrate — After the model has been validated, the last step will be data integration. You will input the structured data into a system, such as CRM which can be used for future purposes.

What NLP techniques are used in this process?

As mentioned, the IDP process is used to easily extract information and classify it accurately. The NLP tools used in this process include:

Named Entity Recognition — locates and classifies named entities found in the unstructured text. This can be a person’s name, organization, location, time expressions, monetary values, percentages, and more.

Named Entity Linking — this tool is used to help identify and match unique entities. It is also referred to as Entity Linking, Named Entity Disambiguation (NED), Named Entity Recognition/Disambiguation (NERD), or Named Entity Normalization (NEN).

Semantic Matching — this tool is used to identify the relationship between data points improving the model’s performance. It determines in a semantic manner if two or more data points have any form of similarity.

Text Classification — the simplest form of NLP which is used to identify text from documents such as passports, proof of address, and more.

How does this help with compliance?

When you think of compliance checks, it’s a lot of documentation screening that needs to be accurately reviewed. This can be for passport checks, mortgage loans, applications, and more.

These NLP techniques can be used for:

• Identity verification
• Collating customer information
• Managing customer information
• Monitoring financial transactions
• Customer list screening
• Checking invoices and receipts
• Inputting Customer Data into the CRM
• Customer service used with bots

All the above processes are time-consuming and costly — especially with the shifts due to the pandemic. Not only does ML reduce the amount of time spent manually completing tasks and cut back your costs, but it has also proven to show an overall increase in compliance performance.

It is not the most innovative implementation a company can make to see an increase in its revenue and performance, but it is vital for organizations that want to improve their compliance checks and strategy to avoid fines and penalties.