BANKING

Fraud identification rate improved for POS, ATM & CNP

Quadratyx built one-of-its-kind next-generation fraud detection tool using the latest ML models & big data technology. We used effective sampling strategy to create the training set data with an ability to self-learn and detect fraud and non fraud data based on complex patterns hidden in the data.

Big Data Analytics & AI in Banks

Acquisition of New Clients in Banks Using Big Data

Augmenting Day to Day Operational Efficiency Using NLP Text Mining

Payment Default Prediction Using Machine Learning

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Banking is at the cusp of a new era – social media revolution, new technologies (open banking APIs, blockchain, IoT), and changing regulatory requirements are together creating an imperative change. And if banks do not align products and services to customer’s present day needs they are at risk of becoming obsolete and redundant.

Banking is a data-driven business

With recent development in technology, data is pouring in from every single transaction and interaction. But the real challenge banks are facing today is how to collect the data and create relevant user experiences to stay ahead of competition.

With ever increasing regulatory requirements, cyber-attacks or frauds, data security concerns and inefficient IT systems, banks are facing never ending operational challenges. In order to survive in this hyper connected society, its critical banks have the right technology architecture – from AI and ML to integrate massive volumes of big data from wide range of sources that enable real time analytics.

With Quadratyx, you have a trusted partner who can support you to respond to these technology disruptions by help build integrated systems to connect and support at every decision point to meet specific business goals.

Fraud risk management

With fraud techniques getting sophisticated every day, there is massive need to keep consumers and banks safe. Machine learning algorithms can help detect fraud in almost near real-time, learn from historical transactional data and from the rich & detailed data sets that consumers are generating, by scrapping the web. Building scalable and efficient solutions to identifying defaults, patterns or a linkage that establishes intent of committing fraud.

Security

How do you build an anti-money laundering system, that reduces compliance cost and improves transaction monitoring process. The pressure to secure financial data has never been so intense. AI offers real valuable techniques in classification – treating incoming data as a stream as opposed to collecting at every point to detect anomalies, processing data relatively quickly in real-time – to proactively identify potential fraudulent threats, mitigate risks and maintain regulatory compliance.

360-Degree customer insights

In today’s world, customer interactions with banks are all around the place – through an ATM, on mobile devices, online banking, social media platforms, etc. Unlike in the past, when all banking interactions where through the bank.

AI and ML technologies enables banks to take all these different interaction data points to anticipate customer needs, understand their financial situation and anticipate where they are going next to drive personalized experience the way that a bank manager used to know but at a much bigger scale. A 360-degree customer view drives down acquisition costs, prevents switching, and increases lifetime value.

Regulatory compliance

For Banks of any size, cost of regulatory risk and compliance is huge – especially with ever-changing regulatory requirements. Traditionally, many manual hours are spent in deciphering new regulations and then compared them with current internal bank policies. Instead, AI can operate in augmented intelligence mode – crunching the data in order to support the compliance officer thereby reducing the burden.