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Case study

Empowering Digital Transactions with AI: A Look at Fraud Detection and AML5.0 rating5 Stars Rated On Clutch

Industry

Financial Services & Banking

Services Used

Artificial Intelligence (AI), Digital Transformation, Large Language Model (LLM), Natural Language Processing (NLP)

Region

Europe

Engagement Model

Agile Based Iterative Delivery

Function/Department

Customer Service and Support

About Client

  • A fintech company based in Stockholm, Sweden.
  • Specializes in digital payment solutions for small and medium-sized businesses.
  • Operations are spread across Northern European countries such as Sweden, Finland, Denmark and Norway.

Problem STATEMENT

  • Challenges in detecting fraudulent transactions.
  • Error-prone and time-consuming process of manually reviewing the transactions.
  • Lack of a system to detect and prevent money laundering activities.
  • High rate of false positives and false negatives.

Solution

  • Collected and prepared a large dataset of valid and fraudulent historical transactions. This was used to train the AI system. The data was prepared for training by utilizing pre-processing techniques such as data cleaning, data normalization and feature engineering.
  • Machine learning algorithms were utilized to analyze the transaction data and behavioral data, and identify patterns linked to fraudulent activity
  • The AI-based solution was then integrated into the current system of the client and transactions were screened in real-time.
  • The system was monitored continuously and used techniques like A/B testing to adjust to new patterns of fraudulent activity.
  • AML (Anti-Money Laundering) checks and monitoring systems (such as solutions for customer identification and verification, transaction monitoring, reporting suspicious activity to stakeholders, etc.) were implemented to detect and prevent money laundering activities. The system alerts the AML team of suspicious transactions that need to be investigated. Collaboration with the team is required to establish alert thresholds and assess system performance.
  • A chatbot was built using natural language processing (NLP) and machine learning technologies to aid with compliance (KYCs) and fraud detection, and to assist customers. The chatbot was integrated with the AML system and was trained to detect phishing scams and other suspicious activities. Other than that it was trained to assist customers with common queries such as account balance inquiries, transaction history, etc.

Business Impact

  • The rate of fraudulent transactions on our client’s platform reduced significantly by 47%.
  • The system’s accuracy rate to detect and prevent fraudulent activity increased to 98%.
  • Implementation of AML checks resulted in a 24% decrease in money laundering activities.
  • The introduction of AI in digital payments improved the user experience as the transactions were faster and more secure.
  • The chatbot reduced 19% of customer service requests and inquiries.
  • Manual effort and time required for reviewing the transactions were reduced to 12%.

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Frequently Asked Questions(FAQs)

How do I know if a solution like this is right for my business?

A solution like this is a good fit if your teams spend more time collecting and reconciling data than acting on it, if reports from different departments do not match, or if decisions depend on delayed or manual reporting. It also fits when your data is growing in volume or sources, and your current setup struggles to keep up.

DataToBiz starts with a discovery call to review your goals, data sources, and reporting gaps. You get a clear view of the expected outcomes, effort, and timeline before any commitment.

Can DataToBiz customize the solution for my industry and current data setup?

Yes. Every solution DataToBiz delivers is configured around your industry, business processes, and existing data environment. The team assesses your current systems, data quality, and reporting needs first, then designs a solution that works with what you already have instead of replacing it. This applies across industries such as manufacturing, retail, healthcare, finance, and logistics, and across setups ranging from legacy databases to cloud data platforms.

Which tools, platforms, and data sources does DataToBiz work with?

DataToBiz works across the major analytics, cloud, and data engineering stacks. For BI and visualization, this includes Power BI, Tableau, and Looker. For cloud platforms, it includes Microsoft Azure, AWS, and Google Cloud. For data warehousing and processing, it includes Snowflake, Databricks, and SQL-based databases. Data sources include ERP and CRM systems, spreadsheets, APIs, IoT and sensor data, and third-party applications.

If your stack includes a tool not listed here, the team can assess compatibility during discovery.

How long does a project like this take with DataToBiz?

Most projects with DataToBiz take from a few weeks to a few months. The timeline depends on how ready your data infrastructure is for the initiative and how many systems need to connect. Smaller solutions go live quickly, while larger programs roll out in phases so you see value early. After an initial discovery call, you receive a timeline with clear milestones.

How does DataToBiz keep my data secure and compliant during the entire engagement?

Security and governance is part of the plan from the first conversation. DataToBiz works within your security policies and follows the regulations that apply to your industry. The project usually Access stays limited to the people who need it, and confidentiality terms cover the engagement from kickoff to handover.

What support and results can I expect after the solution goes live?

The work does not stop at go-live. Your team gets a proper handover and guidance on using the solution, and DataToBiz stays available for questions and improvements. The business impact of the project is measured against the goals set at the start, so you can see exactly what changed for your company.

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