Today, financial services face an unprecedented challenge. They must meet the expectations of consumers who expect instant, accurate, and secure transactions in the blink of an eye. They do so in the face of exploding data growth that will not slow or yield. Real-time data continues to grow by 30% per year, with overall data projected to grow to over 180 zettabytes by 2025.

Companies need to generate insights in real-time to unlock their full potential. According to industry projections, nearly a third of all data will be in real-time by 2025.

Analysing real-time data is critical to stay ahead of the competition. It allows businesses to respond quickly to ever-changing market demands and customer needs.

With the evolution of fintech companies such as PhonePe creating a cashless society, customers expect fast, personalised, and convenient experiences.

Real-time data enables financial institutions to meet these expectations by providing up-to-date information about customer behaviour, market trends, and risk factors, allowing institutions to make informed decisions quickly and efficiently.

For financial services companies to thrive in a highly competitive digital environment, they need to connect and streamline the flow of information across an exploding array of data sources and datasets.

To manage this dichotomy, a real-time multimodal NoSQL database that can handle terabyte to petabyte-scale data is required.

This sophisticated database captures and analyses streaming data in real time to allow for robust AI and machine learning analysis. It must do this at hyper-scale (from gigabytes to petabytes) while ensuring the integrity of every financial transaction. 

Fraud prevention, customer 360, and personalisation

The growing use of online financial transactions also leads to rising cybercrime. Cybersecurity ventures expect global cybercrime costs to grow by 15% per year over the next five years, reaching $10.5 trillion annually by 2025, up from $3 trillion in 2015.

Fraud prevention is the foremost concern for today’s financial services and payment companies. They should immediately validate digital identities and prevent fraudulent transactions without causing customer friction or inconvenience online. 

Fraud prevention requires machine learning at the edge of the network. The real-time, multimodal NoSQL database analyses millions of events, billions of data points, and petabytes of historical information in milliseconds.

For instance, PayPal uses a modern, real-time database to prevent fraud, saving millions of dollars in fraud losses. With a vast global network of users and transactions, PayPal uses a custom-built solution capable of analysing billions of records within 20 milliseconds to determine fraud risk.

Reducing fraud can save millions of dollars per day and empower organisations to provide more personalised services, reduce customer churn, and strengthen customer trust in the brand.

India has nearly 80,000 financial service companies, and globally, there are about 10,00,000 financial service companies. In this highly competitive sector, financial institutions must differentiate their brand when consumers come to their site or risk losing their business.

The modern, real-time database creates a comprehensive customer 360 and personalisation experience to capture and retain consumers. Using AI and machine learning, the modern real-time database builds a connected 360-degree profile of each customer and delivers immersive, personalised experiences to tens of millions of users globally.

A large bank in India uses a modern database as an operational data store by combining data from various channels, payment systems, and core banking systems. It applies propensity models to precisely determine a rank-ordered list of offers based on eligibility and behavioural attributes, serving as a unified personalisation/cross-sell engine for all the channels.

As a result, the bank has differentiated itself as one of India’s most admired banks among Gen Z and millennials.

How to stay competitive

Chief data officers of financial institutions should focus on several things when reviewing their systems to ensure they have a modern data architecture to meet business and consumer expectations.

1. Improve data accuracy: Be sure you refresh market, customer, and transactional data frequently from multiple feeds simultaneously.
2. Speed at any scale: A modern data architecture handles millions of transactions per second while scaling to meet petabyte-range data volume needs. 
3. High reliability: You want high availability with demonstrated uptime of five 9s and strong data consistency.
4. Low TCO: Lowering and managing costs during rising infrastructure costs is paramount. It can be achieved by leveraging a modern database to reduce server usage by up to 80%, saving millions of dollars annually.
5. Offload mainframe workloads: Support ever-growing workloads with a distributed, intraday real-time operational data layer that transforms financial services with real-time data and analytics.

The future is here today

A recent Salesforce survey, titled Future of Financial Services, revealed customers want more of the same—intuitive, custom digital experiences that emphasise their overall financial well-being. 

Highlights include:

• 15% of insurance customers strongly agree their vendors are invested in their financial well-being. 
• 78% of banking customers initiate relationships on a website or app.
• 32% of wealth and asset management customers are extremely satisfied with the firm’s ability to swiftly resolve customer issues.

Leveraging a modern real-time database will allow financial services firms to deliver the expectations of a fraud-free transaction and a complete customer 360 experience today and tomorrow. As the market and expectations grow, these databases will grow to avoid costly re-platforming.