Transaction monitoring and suspicious data detection solution
About the Client
A uniquely positioned data science and intelligence insights product company creating AI-based business applications for data-intensive organizations that impact top and bottom lines. They are recognized as a global leader in Anti Money Laundering (AML) and Know Your Customer (KYC) solutions.
The company cooperates with financial organizations, enabling them to acquire, own, control, manage and personalize their data assets, changing how enterprises monetize them by building unprecedented knowledge graphs, recommendations, and actionable insights. In some cases, they extract the insights using AI-powered algorithms or by applying strict rules to the data.
Hiring a dozen specialists with particular skills and affordable rates in one sitting is still challenging, even for a successful start-up. The current company’s capacity barely covered the rise in demand from their clients. So, the need to scale the company made them search for specialists with relevant expertise.
Another big challenge for the financial sector is compliance with GDPR data regulations. The GDPR defines an array of legal terms at length, significantly complicating data processing for any 3rd party.
Our client has two main products: the first is an enterprise AI Operating System, a big data, cognitive computing, and contextual analytics system covering the entire data life cycle named as one of the best Know Your Customer (KYC) solutions; and the second is the complex analytics builder that can be customized depending on customers’ features enabling concept empowering to Prototype Enterprise AI Application in just one setting.
Since the product development and implementation of new features required specific skills, the company reached Quantum to expand its teams with developers and data engineers with eligible expertise.
In almost a year of cooperation, our contribution to the product has grown several times. The Quantum team has proven to be a qualified technology partner capable of delivering complex solutions and leading multiple development streams.
Working with sensitive data in financial institutions may not seem a big deal, but in fact, specialists always face many duties, regulations, and security rules to follow. In addition to possessing excellent programming skills, multiple tools, and techniques, the specialist also should be capable of analytical thinking and have attention to the smallest details so as not to miss the deviation in the identified patterns and trends in datasets.
In this project’s scope, our team has developed a transaction monitoring system from scratch. The process provides analysis of transaction flows in search of suspicious transactions that can be detected using ad hoc scripts written in a special domain-specific language (DSL). After interpreting those scripts, the system applies the extracted rules to millions of transactions, analyzing individual and contextual information.
To comply with GDPR and other data requirements, we had to perform in-house data analysis on the client’s secure perimeter, collecting, processing, and saving data within the customers’ infrastructure, thus ensuring the highest level of security for sensitive data.
Since a product of such complexity required several development streams, it was crucial to maintain the integrity of the multicultural teams involved. Quantum always aims to focus on continuous system improvement by developing new features and tasks. But in this case, our corporate culture also played a significant role, enabling us to establish synergy with the in-house team, build smooth and flexible collaboration processes, better perform tasks, consider risks, and fix the scope. With this meticulous approach and deep commitment to every challenge, we have been tasked with leading two other teams in addition to ours and working closely with stakeholders.
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Flask, FastAPI, SqlAlchemy&alembic, Perfect, Docker, and Docker Compose for local development. Deployed Kubernetes cluster to Microsoft Azure cloud.
Graph DBs(such as Neptune&Janus and raw RDF files), Service Bus/Event Grid and SQS/SNS, Serverless computations, classic relational DBs(MySQL, MariaDB, PgSql), and different types of storage (S3, Azure Blob).
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