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Four ways financial services companies use big data

Four ways financial services companies use big data IMG
Four ways financial services companies use big data

Big data is rapidly becoming the key driver in the financial services industry. Big data covers a lot of areas: transactions, customer accounts, vendors, and more. All include individual fields of data, from time stamps to payment amounts to unstructured text fields of additional data (such as call center notes). Consider these numbers: the volume of digital banking users has increased from 20% in 2010 to 61% in 2018—more than tripling in a number of years. At the same time, the number of connected devices in the past decade has grown exponentially, with more than 90% of data driven around the digital world being generated in the past two years alone.

The majority of people are accessing their money digitally, and the use of smart devices—be it phone, tablet, laptop, or even web-connected appliances with purchase capabilities—is growing exponentially. 

Handling countless transactions per second requires robust security measures to safeguard against potential threats and ensure data integrity.

In a world where every device is a potential access point for hackers, reliable security measures become imperative for safeguarding data.

Fortunately, the financial services industry is already on top of this. Many of the world’s biggest providers are leading the charge by combining big data with machine learning (ML).

Not only does ML make your money safer, it delivers a better customer experience.

Let’s take a look at four specific ways the financial services sector is integrating big data into everyday operations.

Fraud Detection

The digital age has transformed the way fraud works—not just from people unscrupulously trying to steal, but also the security teams attempting to protect customer money.

Today’s economy is run via online transactions and transfers, which means that for fraudsters, gaining access (usually by stealing someone’s identity or credentials) is the goal.

They attempt this in a number of ways, from skimmers on PIN pads to malware transmitted online to brute-force hacks of accounts.

On a macro scale, that data can tell a lot about the different parties involved; patterns can create expected profiles and, more importantly, identify when potentially fraudulent activity occurs outside of those expectations.

While the finance industry can’t protect everyone at every transaction, they can at as both a safety net and firewall against these types of bad actors thanks to big data.

Challenges

To properly process this volume of data, various transaction datasets—with additional information such as interaction events and customer behavior—must be consolidated.

That means storing data in an appropriate repository, such as a data lake, and applying ML to efficiently crunch the data while identifying patterns.

Financial Regulatory and Compliance Analytics

Regulatory compliance has been an issue for financial institutions since their inception. But in the digital world, regulations have rapidly changed. In addition to working within a digital landscape, regulations have quickly evolved to get a handle on new issues such as an increasing amount of cross-border transactions and the rise of cryptocurrencies.

Big data aids financial services by processing large datasets and facilitating swift rule adjustments to ensure compliance with evolving regulations.

Big data collection forms the compliance foundation, offering real-time proof of regulatory adherence or identifying issues for prompt resolution.

Compliance departments oversee and streamline workflows, minimizing human error, ensuring efficiency, and adapting to evolving regulatory requirements.

A prime example of this comes from Caixa Bank, which saved 60,000 work hours overseeing Spain’s direct debits process.

Challenges

Similar to fraud detection, regulatory compliance requires bringing together multiple sources. Compliance requires fast generation of risk models without disrupting other projects, necessitating efficient utilization of resources and technologies.

Improve Customer Service Through Big Data

Any organization’s operations can achieve valuable improvements with big data, and the financial services industry is no different.

Consider the steps along any workflow; externally, banks and organizations are looking at customer retention and activity on loans, special offers, balance transfers, and other types of financial offerings. Internally, these same organizations are looking for any sort of process improvement, whether it’s in HR, IT, marketing, sales, or any other organization.

Big data provides insights that lead to innovation. Let’s take the example of maximizing customer engagement.

Big data analyzes customer transactional data and account history to identify purchase patterns, geographic locations, and potential engagement triggers.

ML models analyze customer data to identify needs and extend offers maximizing engagement potential, enhancing personalized marketing strategies.

ML models drive personalized offers; for instance, identifying remodeling projects may prompt home equity line offers, enhancing customer engagement.

Challenges 

For a comprehensive customer view, financial institutions must leverage diverse sources, including licensed third-party data on demographics and geography.

Data scientists refine customer models and analyze broader economic factors like interest rates to ensure ongoing accuracy and relevance.

Anti-Money Laundering Strategies

Financial services firms, amidst pressures from governments, face heightened scrutiny on anti-money laundering laws, underlining the importance of compliance.

Money laundering is a different issue from purely fraudulent transactions, and laws and regulations targeting this sort of thing have a much wider scope, including tax evasion, public fund corruption, and market manipulation. Other elements involve concealing these crimes and any money derived from these actions.

For AML compliance, data must be ingested from extremely diverse sources (sanctions lists, legal data, transactions, application logs).

ML models must analyze known money-laundering techniques across timing and context, flagging items for deeper investigation to combat nuanced criminal tactics beyond rule-based thresholds.

ML’s evolving models adapt to increasingly sophisticated criminal schemes, adding substantial value to anti-money laundering efforts.

Challenges 

AML compliance demands diverse data sources, from structured to unstructured, necessitating thorough examination to identify potential risks effectively.

Models have to be built to meet the latest regulations, along with constant updating to maintain compliance. Other elements include using tools such as graph analytics to reveal hidden relationships.

Other Big Data Use Cases

Big data and ML offer transformative benefits across industries, providing insights and efficiency beyond finance. Explore their potential today!

To learn more, take a look at Oracle’s Top 22 Use Cases for Big Data. Covering manufacturing, retail, healthcare, and more, this ebook provides insights into the power of big data across multiple industries.

Explore Oracle’s Big Data offerings for insights and solutions. Subscribe to the Oracle Big Data blog for the latest updates!

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