How Data and Analytics Are Transforming Financial Services in the United States

In the current era of modernization, data analytics is altering commercial and technical circumstances in the U.S. financial services market. Every day, many economic events occur, and the financial sector is heavily involved in forecasting them. It generates everyday data and millions and millions of financial transactions in the financial services industry.

Financial institutions like banks, finance and leasing companies, trading companies and loan providers regularly produce a lot of data. It is essential to have a data handling language that can fully control and analyze this data if you want to glean insightful information from it. Analytics and data become necessary in this situation. To meet evolving and expanding customer expectations and remain competitive in the burgeoning FinTech market, the U.S. finance sector must utilize this enormous amount of data. Data sets must be used by financial institutions and insurance firms to improve their knowledge of their customers.

Data analytics are becoming more widely used in the financial industry thanks to the growing number of online users. For instance, Bank of America said that over 2 million new active digital clients were added in 2021, setting a record for a single year. By February 2022, the bank's total number of verified digital users was over 54 million. In 2021, customers of the bank used its digital platforms a record 10.5 billion times, an increase of 15% from the previous year.

The US financial system, including how consumers lend money, invest, choose loans, support startups, and even purchase insurance, is transforming as a result of FinTech thanks to data and analytics. One in three people who use digital media regularly use two or more financial technology services. FinTech companies in the United States received USD 59.8 billion in investment in 2019 from 1,144 mergers and acquisitions (M&A), venture capital (VC), and private equity (PE) agreements.

In the past, financial institutions did not prioritize structuring their internal data for providing financial services, which hampered their ability to take advantage of analytics that may have benefited their businesses. The data has been around for a while. However, they haven't been able to make sense of it due to challenges in finding the appropriate software, services, and technology. The competitive landscape of industries is continuously being redefined by data analytics. According to an Accenture study, 79% of company executives believe that failing to use big data could cause businesses to lose their competitive edge and possibly even go out of business.

Fortunately, times have evolved. New technology has emerged, and the industry has changed. Businesses can now use data to create better client interactions thanks to analytics in the financial services industry. With offerings that are precisely targeted to match a client's needs in context, they can better compete in the market and offer more complicated offerings on a much greater scale.

The Evolution of Data and Analytics in Financial Services

Data management is a constant challenge for businesses. Financial services firms can reduce needless human engagement that could result in missing a customer's requirement by integrating data analytics that rely on automation, artificial intelligence and machine learning. Technological developments have made it easier for customers to select a particular service early in decision-making, forcing organizational readiness, streamlined customer operations, and increasing income streams.

In today's market, for instance, credit card issuers frequently advertise pre-structured product offers that are dispersed through their digital channels. The consumer faced with a bewildering array of possibilities will turn to technology that can analyze a thorough understanding of their circumstances to present the best option. This effective application of analytics spans the core objectives of businesses, including finding new consumers, upselling, determining credit risk, and onboarding clients.

The capacity to rely on an AI-based platform, frequently personified as a customer-service bot, to provide improved services is often the result. These quicker results put financial services organizations in a competitive marketing position. Any financial services corporation can make quicker, more strategic decisions based on comprehensive datasets and facts by establishing data analytics as a fundamental company value, improving customer service, marketing more goods, or improving fraud detection.

No matter how appealing a human encounter may be in the financial services industry, new challenges will arise due to climate changes. Financial institutions have the tools to remove these hurdles thanks to data analytics, machine learning, and artificial intelligence.

Data Analytics in the Digital Era

Data has always been a driving force in the financial services industry. Every day, millions of commercial transactions are logged. The amount of data produced by the finance industry is not just substantial, but also in real-time. Financial firms have access to enough data to reconsider how they conduct business. The banking industry is working to implement a fully data-driven strategy for expanding their business and improving client experience in today's digital age, which can only be accomplished using data analytics. The field of financial analytics is expanding right now. According to a report by Mordor Intelligence, the financial analytics industry was estimated to be worth USD 6.32 billion in 2020 and is expected to increase to USD 11.02 billion by 2026.

How Data and Analytics Are Transforming Financial Services

How businesses and industries work is fundamentally changing due to the exponential rise of technology and increased data collection. Due to its inherent data-intensiveness, the financial services industry presents a good chance to process, analyze, and use data. Humans used to crunch figures manually, and judgments were based on findings drawn from assessed risks and statistical patterns. However, computers have recently supplanted such capacities. As a result, the financial sector is one of the most promising markets for data-driven technologies.

Here is a look at how combining data and analytics transforms financial services.

Delivering Personalization

The financial services industry uses data and analytics to understand client demands better and put those needs ahead of business needs. Facilitating client segmentation and offering customers better services and solutions increases the viability of financial institutions. It is essential because financial institutions must constantly switch from business-driven to customer-driven models in their action plans. Data analytics, which also improves group and data analysis, makes it simple to execute such tasks. What is evident is how anxiously consumers anticipate their offerings. Since privacy is embedded in everything - they want personalized experiences across many channels. According to Boston Consulting Group, 80% of customers demand personalization despite being wary of their data.

Robotics Automation

Implementing robotic process automation in financial services can result in better efficiency, fewer errors, and higher workforce productivity. According to a Gartner poll, more people are working in the digital finance sector. From the present 50%, it is anticipated that 88% of controllers will have integrated robotic process automation during the next two years. According to Juniper, by 2023, robotic process automation in the financial technology sector is anticipated to generate $1 billion in revenue.

Security and Fraud Detection

The implementation of cloud computing technologies raises serious privacy protection concerns. As FinTech advances, cybercriminals are developing cutting-edge strategies for online fraud.

Financial organizations with massive data sets have been pushed to incorporate fraud detection tools into their risk-management plans. According to Infopulse, 75% of respondents that have included AI and ML in their risk management strategy employ fraud detection backed by Big Data analytics. Financial institutions constantly work with client data in the current environment. Not only is the information important, but it is also worthwhile since it provides insights into how financial institutions conduct their daily operations. This type of sensitive data requires regular risk assessment, protection, and substantial risk reduction while being shielded from fraud. Fraud detection and prevention are carried out via machine learning, fed by data. Analytics that decipher purchasing trends has reduced the security issues that credit cards originally posed. The credit card company can quickly freeze the card and any fraudulent activities when secure and valuable credit card information and verification is taken. In addition, they can alert the client about security risks.

Risk Management

Managing risks has always been difficult for the financial services industry, particularly operational, fraud, and credit risks. Data enters the picture at this point. Data analytics is used in many important risk management contexts. Advanced data analytics tools and methodologies can improve system response time, dramatically increase the predictive capacity of risk models, and provide more comprehensive risk coverage. In addition, data analytics can be very helpful to the finance sector in complying with legal and regulatory requirements relating to integrity and credit risk. Therefore, the impact of data analytics in the financial services sector is huge, especially in risk management.

Stock Market and Investments

The way venture capitalists and stock markets operate is changing due to data analytics. When given data, machine learning, which uses computer algorithms to detect patterns in vast amounts of data, enables computers to make decisions similar to those made by humans and make accurate forecasts. It also allows computers to execute transactions swiftly and continuously. Data analytics keeps track of stock movements and includes the best prices, enabling analysts to make wiser choices and results in minimal to no manual errors. The ability to successfully reduce the risks associated with financial trading is made possible by access to vast data sets and greater algorithmic knowledge, which leads to more accurate predictions.

Customer Loyalty

Consumer loyalty is highly influenced by data analytics in the financial services industry. According to 80% of senior banking sector decision-makers polled by Sapio Research, consumer-facing features generate revenue and are a significant differentiator. It's trickier than it looks to maintain customer satisfaction and loyalty in a highly regulated industry disrupted by technology. According to 65% of respondents, data analytics enables them to offer personalized services to their customers and predict their behavior in the future.

The finance sector needs to keep up with data analytics and use it to its advantage.

Applications of Data Analytics in Financial Services

Data analytics have a significant impact on transforming financial services. Everything you can plan, automate, and the forecast is worth its weight in gold. For instance, with apps in finance powered by data, the following can be seamlessly executed:

Forecasting Financial Trends

Financial trend forecasting can greatly impact how management approaches future decisions. In contrast to being caught off guard, being proactive can reduce the harm a bad financial trend can have. Additionally, data analytics might give you an advantage over your rivals.

Analyzing Potential Risks

Using data analytics in finance enables you to understand the potential threats to your company. Additionally, you can give clients advice about their difficult circumstances. Machine learning algorithms can identify risky assets rather swiftly. It is a significant change to avoid making careless financial choices and to think twice before starting a financial disaster.

Automating Tasks

The job of financial services managers and analysts is streamlined and made more productive by automating processes. Determining if a specific consumer is a financial liability, for instance, is made considerably faster by online apps and algorithms. Financial analysts will find it less difficult to decide whether or not to offer that customer credit or services.

Foster Inclusivity and Diversity

Nowadays, equality is a topic of significant discussion. And it makes sense to treat everyone equally, regardless of ethnicity, personal beliefs, sexual preference, or gender. Therefore, transparency, qualification, and the absence of bias are all made feasible by data analytics in the financial services industry. Machine algorithms are all the same. They exist to help, and they aid financial institutions in performing their duties seamlessly.

Conclusion

More and more financial institutions in the United States are anticipated to use data analytics in the upcoming years to monitor and regulate data to create effective, intelligent firms and take advantage of new prospects. Financial services are not an exception to how data analytics has steadily dominated several businesses in a relatively short period. FinTech companies have finally realized that to maximize benefits, it is essential to utilize generated data fully. Additionally, data analytics in the finance sector improves efficiency, offers outstanding solutions, and fosters a customer-focused mindset inside the sector, all while reducing the risks and fraud prevalent in the industry.

NETSOL's Innovation Lab and Research and Development teams continue innovating and working on significant technologies such as big data, artificial intelligence and machine learning, and other technologies that continue gaining traction in the global finance and leasing industry. The company finds it necessary to continue building proof-of-concepts using the latest cutting-edge technologies to enhance current offerings and to continue ideating and creating modern technology products for NETSOL's customers worldwide.

If you would like more information about our products for the North American and global finance and leasing industry, you can explore them by clicking here or contact us by clicking here.

Further, if you would like a demo of our premier, modern technology platform NFS Ascent (Available on the Cloud via flexible, subscription-based pricing) that futureproofs finance and leasing operations, please click here.