Big Data analytics for banks: how to improve the accuracy of counterparty verification

In today's world, banks process a vast amount of data every day, including information about customers, transactions, companies, and markets. Every year, this data becomes increasingly abundant, and at the same time, the requirements for verifying counterparties— customers, partners, and suppliers —quickly and accurately are growing. Simple verification methods are no longer enough. Manual verification of documents, phone calls, or inquiries into state registers is time-consuming, inconvenient, and often inaccurate.

At the same time, banks must comply with strict requirements ranging from Know Your Customer (KYC) rules to anti-money laundering (AML) and sanctions control. Failure to comply with these requirements can result in heavy fines, reputational damage and risks to the entire business.

That's why more and more banks are turning to Big Data analytics - tools that allow them to process large amounts of information automatically, quickly and with high accuracy. With the help of such solutions, it is possible to analyse transactions in real time, check connections between companies, find suspicious behavioural patterns, and identify risky counterparties before they become a problem.

У цій статті ми розглянемо, як банківська аналітика великих даних допомагає посилити фінансовий комплаєнс, краще виконувати перевірки контрагентів і відповідати вимогам регуляторів — таких як НБУ, FATF та європейські директиви з протидії відмиванню грошей.

Why banks need Big Data analytics

Modern banks operate in a high-risk environment and are under constant pressure from regulators. At the same time, digital verification of customers and partners must be not only accurate but also very fast, as it affects the security, reputation, and competitiveness of a financial institution. This is where Big Data analytics comes to the fore - a technology that allows processing and analysing huge amounts of information to identify risks before they cause damage.

Here are three key reasons why banks should implement big data analytics today:

1. Diversity of data sources

Previously, banks relied mainly on internal systems: personal data, credit history, and standard checks. Today, this is not enough. Big Data allows you to combine data from dozens of external sources and see a complete picture of risks.

In particular, banks can use:

• Access open state registers such as the Unified State Register, the register of court decisions and sanctions lists. This helps to identify links between companies, beneficiaries and litigation, or signs of fictitiousness.
• Transactional data analysis involves examining the client's transactions in terms of volume, frequency, countries and counterparties. This allows us to detect suspicious activity.
• Behavioural patterns include how the customer uses online banking and mobile applications, as well as any changes in their habits. Sudden changes can signal a threat.
• Social networks: in some cases, it is useful to check publicly available information, for example whether information on social networks matches that provided in the questionnaire.
• Commercial databases — services such as YouControl, Dow Jones and World-Check provide access to in-depth analytics and company reports, as well as information on connections to sanctioned persons.

The more data available, the more accurate the analytics and the lower the risk.

2. Increasing regulatory requirements for KYC/AML solutions

Regulatory requirements in the field of financial control (e.g. NBU, FATF, EU AML Directives) are constantly increasing. It's not just the Ukrainian NBU — international organisations such as the FATF and the EU are also constantly updating their rules to combat financial crime.

Big data analytics helps banks meet these requirements by:

• Digital customer due diligence: quickly and automatically check new customers for risk.
• Automate KYC processes to reduce the need for manual work, as checks are performed according to pre-configured algorithms.
• Transaction monitoring: instead of only checking customers when they open an account, banks can track suspicious activity and verify data in real time.
• Changes in companies can be analysed, for example, if the beneficiary, address or share capital changes. The bank can then record this and update the risk profile.

Thus, Big Data does not just help banks to 'keep up with the norms'; it enables them to do so automatically and quickly.

3. Detecting anomalies, connections and evasion schemes

Banks today need to think beyond black and white. In real life, fraudulent schemes are highly complex. For instance, companies may have fictitious directors, intricate transaction chains and connections via front men. Such things are impossible to detect without powerful analytics.

Big data makes this possible.

• Detect anomalous activity, such as transactions that differ significantly from the customer's usual behaviour.
• Find hidden connections: establish who is connected to whom and how risky payments may be made.
• Recognise fraud patterns: machine learning-based systems are trained using historical data and can detect patterns similar to those in known cases of financial crime.
• Prevent risks before funds are lost – real-time analytics allow you to stop a suspicious transaction or block access in time.

This means that, rather than dealing with the consequences, banks can take a proactive approach.

Technologies that change the verification process

A few years ago, verifying counterparties meant manual work for employees: they collected certificates, checked registers and compiled reports. Today, however, everything looks completely different. Thanks to modern technology, this process can be automated, accelerated, and made much more accurate. Below, we describe effective tools that help banks work quickly and safely.

Machine learning in banking

Machine learning (ML) is a technology that enables computers to ‘learn’ from historical data and make predictions. In the banking sector, this means that the system learns to recognise risky customers or suspicious companies.

Rather than a simple 'pass/fail' assessment, the ML model analyses:

• The counterparty's financial performance.
• Atypical behaviour, e.g. frequent account changes;
• Links to other companies, including those with a poor reputation.
• Involvement in court cases or public scandals.

Such an automated system can assess risks more quickly and accurately than any human analyst and works 24/7 without interruption.

Natural Language Processing (NLP)

NLP is a tool that can "understand" text written in a natural language, such as Ukrainian or English. It is required for processing unstructured information, for example:

• news and media publications;
• press releases of government agencies;
• court decisions;
• messages on the official websites of companies.

The NLP-based system can scan hundreds of sources in minutes to find mentions of risky counterparties, such as those involved in criminal cases or on sanctions lists. What's more, it does all this in real time.

API for customer verification

In simple terms, an API (Application Programming Interface) is a "bridge" between the bank and external services, allowing you to quickly obtain up-to-date customer data.

Rather than an employee manually searching for data in registers, the system automatically accesses the necessary sources through the API and receives the data instantly:

• Information from sanctions lists (OFAC, EU, UN, etc.).
• Up-to-date data from state registers such as the USR, as well as from tax authorities.
• Transactional analytical signals indicate whether a client is conducting suspicious financial activity.

This significantly speeds up the verification process and reduces the risk of overlooking something important.

Integration with transaction monitoring systems

In a modern bank, the verification process does not end at the moment of account opening. Clients are constantly generating new data by making transactions, changing contact details and adding beneficiaries. All of this must be monitored.

This is why Big Data solutions can easily be integrated with existing banking systems.

• Core banking systems;
• CRM systems;
• Financial transaction monitoring platforms.

This allows:

• automatic detection of suspicious transactions, even before they are completed;
• update customer profiles after any changes;
• respond instantly to new risks.

The result is constant monitoring without human intervention, making inspections more efficient and less costly.

Typical scenarios for using Big Data in banks

Big Data is not an abstract concept. It is a real tool that is already changing the daily operations of banks. If used correctly, they can improve the accuracy of checks and identify potential threats before they become problems. Here are some examples of how banks use big data analytics in practice:

Detecting connections between counterparties through graph databases

In many cases, fraudulent schemes are not based on a single company, but on an entire network of companies linked by beneficiaries, addresses, directors or IP addresses. To identify these connections, banks use graph databases such as Neo4j.

Thanks to this technology, it is possible to:

• see the structure of a group of companies as you would see a map;
• identify 'padding', fictitious structures, and offshore connections quickly;
• put together a picture that would otherwise go unnoticed.

This is particularly useful in the fight against money laundering (AML) when companies deliberately conceal their true connections.

Identifying the ultimate beneficiaries

One of the most important elements of due diligence is establishing who controls the company. In many cases, the owners are hidden behind intermediaries or nominees.

Big Data allows you to:

• analyse open data from registers, international databases and media profiles;
• automatically compare information from several sources to find the real beneficiary;
• avoid mistakes that may occur during manual verification.

This significantly reduces the risk of cooperating with fictitious entities and saves time.

Check for sanctions and reputational risks

Modern analytics tools automatically scan:

• sanctions lists (OFAC, EU, UN);
• mentions in the news or public reports;
• participation in criminal or administrative investigations.

This allows the bank to notice in time:

• whether the counterparty has been sanctioned;
• whether it is involved in investigations or anti-corruption cases;
• whether there is a risk of a negative impact on the bank's reputation.

This increases the level of financial compliance and helps banks avoid legal and reputational consequences.

Transaction analytics

Bank customers leave a trace in the form of financial transactions — their transaction profile. Machine learning can recognise unusual changes in behaviour:

• Payments of large sums to new countries.
• A sudden increase number of transactions.
• Transferring funds to accounts with a suspicious history.

This is a key tool for:

• fraud detection;
• counterparty risk assessment;
• launching automated checks in case of abnormal activity.

This enables the bank to react quickly, even before any financial damage is incurred.

Benefits for the banking institution

• Reduced verification time. Automated risk verification can reduce the KYC procedure from days to hours, or even minutes.
• The human factor is reduced. Rather than manually analysing hundreds of documents, algorithms are used that do not tire or make mistakes.
• The process is transparent. All the system's actions can be documented and demonstrated to regulators (NBU, FATF) in the event of an audit. This is important for "know your customer" policies and compliance checks.
• Scalability. Analytical tools for banks can be easily scaled up within new products and when entering new markets.

Challenges and how to overcome them

While the introduction of big data analytics into banking processes opens up many opportunities, it also brings new challenges. It is important to be aware of these challenges and understand how to overcome them effectively. Let's take a look at the most common challenges banks face and how to deal with them.

Unstructured data

Much of the data that banks can use to verify counterparties is not in the form of a convenient table with columns, but rather chaotic texts, PDF documents, news articles or open registers. In this form, the data is difficult to process automatically.

How to solve it:

• ETL (Extract, Transform, Load) processes are used to extract, cleanse and structure data.
• Natural Language Processing (NLP) technologies can analyse large amounts of text and identify key facts, names, dates and connections.
• Classifiers and tagging can organise even complex, unformatted sources such as court decisions or media reports.

Integration with banking systems

Big Data platforms cannot exist in isolation — they must work alongside the bank's existing systems, including: These include CRM systems, customer service systems, data warehouses (DWHs) and core banking systems. This can create technical challenges.

How to solve it:

• Modern analytics solutions provide ready-made adapters, software development kits (SDKs) and application programming interfaces (APIs) that allow you to quickly connect to the bank's internal systems.
• Many platforms already provide integration with the most common banking services, so the adaptation process is quicker.
• Conducting a preliminary audit of the technical architecture and involving the DevOps/IT team to establish connections between the platforms is important.

Protection of personal data

Processing large amounts of sensitive information carries significant legal and reputational risks. Violation of the GDPR, Ukrainian legislation or NBU standards can have serious consequences.

How to solve it:

• You need to implement a data governance system with clear rules on who works with personal data, how it is stored and protected, and where it is kept.
• All processes must comply with GDPR standards, as well as the requirements of the NBU, the FATF and other regulators.
• Data must be encrypted, and access to it must be logged. Access control policies for different roles are also required.

A need for skilled professionals

Even the best technology will not work effectively without people who know how to use it. Banks need new competencies, not only in IT but also among compliance professionals.

How to solve it:

• Compliance officers must master digital tools, understand the logic of algorithms and be able to read analytical dashboards and understand data sources.
• Banks are increasingly setting up data analytics departments staffed by data scientists, analysts and data engineers.
• Investing in staff training is important, from internal courses to cooperation with universities and technology partners.

Steps to implementation

Transitioning to big data analytics is not something that can be done overnight. It's a gradual process that requires careful planning, the involvement of specialists, and the testing of solutions. If implemented gradually, the result will be both technologically efficient and practically useful for the bank's daily processes.

1) Assessment of existing processes

The first step is to understand where the bank is starting from. What tools are already used to check counterparties? Where exactly do delays or risks arise?

• Conduct an audit of the current KYC and counterparty verification processes.
• Determine which data sources are used, how long the checks take and which stages depend on manual work.
• Identify where bottlenecks occur: for instance, how long does it take employees to find beneficiaries? Does the bank have time to detect suspicious activity before a transaction is made?

This analysis will help you identify where Big Data can be most beneficial and establish automation priorities.

2) Choose a platform or technology partner

Once you have identified what needs to be automated, you can select the appropriate tools. It is important to consider the technical capabilities and reputation of the developers.

• Below are some international solutions that have already proven their effectiveness in the banking sector:
• KYC policy implementation based on Camunda. Camunda enables the creation of flexible and transparent customer verification processes through the use of compliance rules, integration with external databases and scalability. It is a business process automation platform that can easily be adapted to the bank's needs.
• SAS AML is a powerful solution that provides transaction monitoring and money laundering detection.
• Palantir Foundry is a flexible platform for integrating large data sets, building analytics, and improving decision-making processes.

There are also Ukrainian services that are widely used for verifying counterparties:

• YouControl offers convenient integration with open registers, court decisions and checks against sanctions lists.
• VKURSI is another local solution for verifying business reputation, ownership structure and risk factors.

Select a platform that meets your bank's current requirements and can easily be scaled up in future.

3) Deploy a pilot project

It is risky to start with the entire system at once. It is better to conduct a pilot (i.e. a trial run) on a separate part of the process first.

• For example, you could test the automated verification process for small business clients or freelancers.
• Check how counterparty scoring works and how accurately the new model assesses risk.
• Investigate how the system integrates with your CRM or transaction system.

This will enable you to:

• identify technical problems before scaling up;
• ensure the new approach is effective;
• get feedback from employees who work with the system daily.

4) Staff training

Even the best solution will be ineffective if the team does not know how to use it. Therefore, training is essential.

Organise training for:

• Compliance specialists, so they understand how the new system works, how risk indicators are formed and how to interpret analytics.
• Analysts, so they can set up queries, adapt dashboards and interpret results.
• Risk managers can use big data to predict risks and make decisions.

Training can be delivered in stages, from basic briefings to in-depth data science workshops. This investment will pay off even at the pilot stage.

Conclusion

Big Data analytics is not 'just another trendy technology', but a practical tool that provides banks with genuine benefits. It enables you to verify counterparties more quickly, anticipate risks, detect fraudulent schemes and ensure you are always prepared for regulatory inspections.

In an era of ever-growing data volumes and increasingly stringent financial compliance requirements, manual methods are no longer viable. To stay one step ahead, a bank needs analytical tools that collect and analyse information to provide accurate decision-making insights.

By investing in big data today, a financial institution can protect itself from risks and lay the foundation for growth, product launches and improved customer experience.

It's time to update your approach to counterparty due diligence. Start with small changes, choose a pilot project and discover how Big Data can transform your business.

Want to know more? Contact us at marketing@integrity.com.ua and we'll help you understand the platforms, setup and implementation.