Data is becoming the lifeblood of businesses all over the world, but the banking and finance sector is one where it can make a particularly big difference. It can unearth new levels of insights into customer behavior and preferences across billions of transactions and functions, enabling better decision-making around more intuitive, user-friendly and personalized solutions that drive competitive advantage.
But to take full advantage of that opportunity, it’s vital to understand how to maximize the potential of that data. This blog explores how banking and finance firms can do just that.
transactions increasingly dominate the global financial landscape. According to McKinsey, use of cash declined by four percentage points in 2022 alone, and the growth in electronic transactions has outpaced growth in payments revenue three-to-one over the last five years.
Payment data refers to any information directly connected to a transaction, from amount and method to invoice numbers and identification codes. Every type of this data has the potential, when analyzed by the right tools, to uncover compelling trends around consumer desires and behavior. This can be instrumental for banking and finance firms in removing the guesswork in their decision-making, across product development, personalization, customer service and more.
In order to drive those vital insights from data, it’s essential to have a robust banking data strategy in place. This can be used to ensure that all data is accurate, reliable, consistent and relevant, and to clearly define the overarching business objective of the data and insights. These can then inform the practical actions of collecting, cleaning and analyzing the data involved.
Getting this data strategy right will enable banking and finance firms to:
Gaining accurate insight into the preferences and behavioral patterns of individual customers allows personalized offers and experiences to be created for them. For example, customer spending data from Google Pay is being used to develop targeted marketing and improve return on investment, as well as breeding loyalty and trust that can be hard to develop with the modern consumer.
At a time when cybercrime is ever more complex and sophisticated, analyzing patterns of behavior that can point to fraudulent or criminal activity can inform a proactive approach to risk mitigation. An example of this is Mastercard’s AI capabilities, helping some of the UK’s leading banks predict and prevent payment scams in real-time.
The banking and finance sector is changing all the time, influenced by wider economic shifts, and evolutions in consumer-facing sectors like retail. This makes regular development and innovation of financial products and services vital to drive and retain competitive advantage. Insights from payments data can guide this innovation towards the right outcome through the creation of new and evolving products most likely to appeal to emerging market needs.
Connected to the previous point, pricing of services is now a major differentiator in the banking sector. There are several reasons for this: consumers have never been better-placed to shop around; comparison websites make it easy to find the cheapest options; and agile FinTech startups have shaken up the marketplace. The insights from payments data can be used to inform pricing strategies, potentially including dynamic pricing, so banks and finance firms can perfect the balance between a competitive price point and profitability.
FinTechs can provide the insights from analyzing large datasets in detail, while traditional finance companies deliver the customer reach and scale to which those insights can be applied for innovation. According to Visa, 95% of banks are now using FinTech partnerships to enhance their digital products and reach, while 86% are doing so to save money and accelerate implementation.
As well as making the most of the present, banking payments data is vital for making the most of the future. Putting data through predictive analytics models can forecast how trends are likely to develop in the future, and inform long-term strategic planning accordingly. These are based around complex algorithms that can create predictions around future customer behaviors or market evolutions with a high level of accuracy, and detail beyond the reach of human endeavor.
A data breach at a financial software provider led to the account and personal details of 57,000 Bank of America customers being compromised, emphasizing the need for strong security measures. Embedding security and governance measures into analytical tools is therefore essential to keep customer data safe, avoid breaches that damage trust, and eliminate the risk of costly non-compliance penalties.
Like any data-based technology implementation, there are always some key challenges to overcome. Data privacy and security are perhaps the biggest, given the sensitivity of the information involved and the heightened consumer awareness of data breaches and misuse.
It’s for this reason that strong encryption methods are now essential for all digital payment platforms and gateways, so that payments data is securely processed and stored as soon as it’s provided by the customers. This should come in conjunction with regular updating of security protocols, and further customer education on the risks and best practice around banking online.
These are the key factors that Ciklum helps embed into the technologies we create for banking and finance firms like yours. We ensure that you have the right banking data strategy in place first, then implement innovative, secure analytical tools that extract maximum relevant insight from that data.
Explore our solutions for the banking and finance sector in more detail here, or click on the link below to talk to our team about your specific goals and requirements.