In today’s rapidly evolving financial landscape, the role of data has become paramount in driving transformative macro trends within the financial services sector. A macro trend is a significant and long lasting shift that affects a global population. In the world of financial services, globalization and a proliferation of data have driven a large number of macro trends, even in just the last few years.
This body of work delves into the profound impact of data-driven advancements on key areas such as cross-border transactions, risk management, and the transformation of legacy card infrastructures. By harnessing the power of data, financial institutions are navigating new avenues, optimizing operations, and enhancing customer experiences in ways that were once considered unattainable.
1. Cross-Border Transactions
Cross-border payments are transactions that occur when the buyer and seller are in different countries. Traditionally, these payments were conducted via wire transfers between banks or alternative services, like Western Union. These transactions used the correspondent banking network, which providers used to settle the transactions.
New back-end networks have gained popularity and helped optimize cross-border payments, allowing more ways for payments to be sent to the receiver. In other words, new technology in cross-border payments has reduced the cost and increased the speed of cross-border payments, leading merchants into other world regions.
How Data is Driving this Financial Services Macro Trend
Despite technological advances in cross-border payments, understanding which payment method to use in each region remains challenging. For example, North America prefers credit cards, whereas Southeast Asia uses mobile wallets. Merchants will typically have a few different methods on their store’s front end, allowing the customer to select their preferred payment method.
As merchants expand into the global economy with the assistance of new cross-border payment methods, it will be important that the data they obtain from their customers is secure. Because cross-border payments come with an increased risk of fraud, implementation on a global scale should treat security as a primary concern.
On a positive note, those organizations that have implemented cross-border payments now have abundant data to analyze in driving business decisions. A cloud platform such as Snowflake allows these merchants to store large amounts of data, gain meaningful insights, and provide an overall better customer experience utilizing artificial intelligence (AI) and machine learning (ML) models. By utilizing Snowflake’s data cloud, many resources can be scaled to meet the demand.
2. Risk Management
Within the financial service industry, AI and blockchain can enhance aspects of risk assessment and decision-making. Whether utilized in combination or independently, these two technologies possess the potential to eliminate friction in multiparty transactions and significantly enhance transaction speed. For example, applicants looking for a loan can now give consent to pull personal data from the blockchain and feed it into an AI for loan review. Because the customer information is automated, a bank loan’s approval and closing process can take minutes instead of weeks.
How Data is Driving this Financial Services Macro Trend
Due to the amount of information required, financial institutions need help to take advantage of the perks of artificial intelligence. With cloud technology like Snowflake, scientists can store structured, semi-structured, and unstructured data easily. Suppose there is data in other areas, like Amazon S3 or Azure blob. In that case, Snowflake allows external tables to be used, so that the data can be queried in one place, reducing the time for data discovery and preparation. Snowflake can also materialize features using streams and tasks within Snowflake or other data movement enablers like dbt.
Real-world applications of how the modern data stack has transformed or can transform risk management practices include fraud detection and prevention, credit risk assessments, regulatory compliance, and operational risk management. Modern data stack technology can assist organizations in utilizing advanced analytics, machine learning, and artificial intelligence to detect and prevent fraud in real time. Data can coalesce from various sources like transaction history, customer profiles, etc., which can then be used to build models on the modern data stack to detect or prevent fraud.
3. Legacy Card Infrastructure
Credit cards have been around since the 1950s and have been dominant in the financial service and payments industry for years. In the United States alone, credit cards are used by 48% of business owners and around 37% of consumers. Currently, most credit cards use payment rails, a platform or network infrastructure that allows all digital money transfers between payers and payees, regardless of country, currency, or digital payment method.
Using the payment rail network, the bank acquires the payment once the transaction is initiated. The bank of the card then checks that there is enough available credit to complete the transaction to authorize the transaction. The issuing card bank then sends a message to the acquiring bank to complete the transaction. This system is called the dual message system, and most of these systems are controlled by Visa and Mastercard. Creating a new space is very difficult because dual messaging systems are so complex.
The complexities of the dual messaging system have pushed other countries, except the United States, to adopt other payment methods. For example, open banking has been prominent in the European Union. Electronic payment systems like Venmo, which allows users to link their bank account to the platform to send and receive money, have also become a good option. Finally, blockchain has also been presented as an alternative to avoid paying high fees for accepting electronic payments. Because blockchain technology already allows for payments to be sent, received, and authenticated, small businesses have begun implementing it to reduce costs and fees.
How Data is Driving this Financial Services Macro Trend
The many payment options available allow an easy integration with the many modern data stack tools available, specifically Snowflake. These integrations allow for a smooth transition of data from many different sources. Whether they are from card systems that access various credit or debit cards or any other modern payment methods, Snowflake ensures that the data from different sources is collected into a single source of truth.
The current card infrastructure and other payment method systems need real-time processing power to handle large transaction volumes, providing instant responses. To support these high-volume transactions, Snowflake can leverage methods like stream processing. Snowflake can easily be integrated with stream processing frameworks, which allows real-time data processing and smooth interactions between legacy systems and payment methods. This allows organizations to efficiently process and analyze data as it comes in and enable fast and responsive payment processing capabilities.
Legacy card systems and modern payment solutions might be different in terms of their scalability and performance requirements. However, Snowflake and other technologies within the modern data stack are designed to handle various scalability needs. The modern data stack can handle the large volumes of data and integration needed depending on which method is used.
Moving Away from Legacy Systems with Hakkoda
The dynamics of modern data utilization in the financial services industry have ushered in transformative shifts. Cross-border transactions have evolved from traditional wire transfers to streamlined digital channels, enhancing efficiency and reducing costs. As financial institutions venture into global markets, robust data security measures become pivotal. Snowflake’s data cloud empowers businesses with secure and scalable storage, facilitating advanced analytics and insights generation.
Moreover, combining AI and machine learning with real-time data processing opens new avenues for risk assessment, fraud detection, and seamless customer experiences. Legacy card infrastructures and contemporary payment methods seamlessly integrate within Snowflake’s ecosystem, ensuring a unified source of truth for data and enabling real-time decision-making.
By harnessing Snowflake’s capabilities, financial institutions can navigate the complex landscape of financial services, driving innovation and efficiency across various domains. At Hakkoda, we have experts in the financial services industry who can assist our clients in moving away from their traditional legacy systems to modernized data architecture.
