
Introduction

## Some example Applications, Opportunities and Challenges of Big Data in Financial Services

Big Data is undoubtedly one of the latest innovations making a significant impact on technology and business. Big Data found potential applications in many areas, including healthcare, pharmaceuticals, finance, supply chain, etc. Although Big Data is very relevant in many subject areas, it has a significant impact on the business field of finance. 

Business firms need to hire professionals with finance backgrounds, especially accountancy, to make innovations in this area. The professionals also should possess skills in computers and information technology (IT) to cater to modern business dynamics. Those finance professionals and accountants who have the relevant domain knowledge and relevant IT skills can take full advantage of that, integrating Big Data into decision-making processes.

Billions of financial transactions occur every day globally. The finance field cannot ignore the relevance of big data. Thus, Big Data and analytics are essential tools for the financial services sector. The new data-driven services can leverage increased revenues with reduced cost and improved efficiencies. It can also help improve security and customer service. Globally, Big Data analytics is big business. It is currently predicted that its market will exceed two hundred billion US dollars, making it one of the biggest drivers for the growth of the banking sector.

Modern Big Data empowered FinTech firms are real challenges for the traditional banking industry. It can offer customers fast and low-cost financial services, make and receive online payments, and offer more advanced benefits such as a peer-to-peer lending facility. There is specific evidence that the traditional banking sectors are catching up rapidly, adding Big Data analytics in their business.  

Why the finance industry needs Big Data ?

![](/uploads/closeup-photo-funny-excited-lady-raise-fists-screaming-loudly-celebrating-money-lottery-winning-wealthy-rich-person-wear-casual-172563278.jpg)

Big Data analytics can analyze the financial industry's petabytes of structured and unstructured data to anticipate customer behaviors and create strategies for banks and financial institutions. The financial data sets are too large for conventional database applications to handle in real-time.

In short, by collecting and analyzing vast quantities of data, financial organizations can anticipate the behavior of their customers and develop strategies to provide better, safer, and more relevant, and better services. The value of the data lies almost entirely in the way it is gathered, analyzed, and interpreted.

Opportunities of Big Data in finance

Big Data play a pivotal role in strengthening various aspects of the business. People’s lives are improved by smoothening the business process by utilizing Big Data analytics. Integrating the Internet-of-Things or IoT with the business processes is the most modern direction of big data for real-world use cases. Sensors linked to objects (things) generating meaningful data and information helping in decision-making are known as Internet-of-Things or IoT.

Decentralization of the decision-making process is a salient opportunity available in Big Data studies. The systems are competent, efficient decision-making based on the information available to them and set standards, all with the help of Big Data. The minor details of the financial data are not needed to be checked by the accountants.

Predictive analytics and data visualization is other major utilities of Big Data in the field of finance. The decision-making process is achieved from objective facts and figures when finance practices are dependent on predictive analysis of the financial data. The data analysis processes are simplified in these techniques. The decision-making process has been simplified for accountants and financial analysts by using various data visualization approaches. 

Financial analyses need a lot of refinement of data into information before it is used, so Big Data techniques are adequate for the purpose. Since vast data are to be managed by the accounts in every facet, the big data analytic solutions are primarily based on data-driven accounts audits. Big Data can control and eliminate the involved risk.

The top five Applications of Big Data in the finance industry are:

1. Customer analytics – employed by roughly 55% of the sector: Customer-centered analytics has become a priority, a radical departure from the past when the financial sector was mainly product-centered. The main focus of data insights, systems, and operations is now directed towards the customer base. Knowing the changing markets and customer preferences allows banks and other financial-services companies to quickly develop new customer-centered products and services, seize new market opportunities, and retain customer loyalty. 
2. Risk and financial management – employed by about 23% of the sector: A large segment of the finance sector utilize big data to enhance risk and financial management by optimizing return on equity, fighting fraud, reducing operational risk, and meeting regulatory and compliance requirements.
3. Development of new business models – employed by almost 15% of the sector: Fraud prevention is a critical security responsibility. Fraudsters employ experts and massive resources to develop new tools/ways to commit cybercrime. Big data analytics can incorporate artificial intelligence and machine learning as software weapons in combatting these cyber threats, identifying cyberattacks and fraudulent financial transactions.
4. Operational optimization – employed by about 4% of the sector: The operational cost of the financial sector depends on the large unsupervised data needed to be collected and processed on a regular basis. Big data can serve for overall operational optimization.
5. Employee collaboration – employed by approximately 2% of the sector: The employees form a large part of the stakeholders of the financial sector. Big data can improve the participation of the employees by enriching employee collaboration.

Some examples of the use of Big Data in the financial sector:

* Offering customers credit cards with customized interest rates and incentives which is based on their spending history
* Suggesting consumer products to the customers that offer value and that match their spending patterns.
* Analysis of customers ATM usage and interactions with call centers to increase customer engagement
* Use of speech analytics to determine main reasons for repeat calls to call centers from customers
* Predicting when a customer might close their account and taking steps to dissuade them from doing so

Challenges of Big Data in finance

In the past few years, a large amount of data is being created at a swift pace. The data that has been generated/synthesized over the past few decades has not been generated/synthesized for thousands of years of evolution of the human civilization. 

Data utilization is necessary and essential to make critical decisions. Out of all of the data created and collected, some data are analyzed and utilized. These data can be passed to the company’s tables and increase its revenues, but there is a set of obstacles correlated with the utility of big data in finance.

Investments in data analysis can result from financial companies' struggle and fight for getting positive returns. There could be a couple of grounds that become the basis for negative returns, as mentioned below.

1. Accountants often face some difficulties while inspecting and analyzing the correct finance data. Since garbage data can produce garbage outcomes only, thus collecting, observing, and processing debris data has zero utility. Data analytics have evolved from descriptive to diagnostic, predictive, and now gradually to perspective analysis, which has usages in identifying ideal solutions. These analytics produce superior outcomes for data processed and collected from a reliable source. The financial analyst must make judgments on the data types being used. Understanding the correct type of data may overcome this problem.
2. A faulty data strategy may be one of the major issues. If the company expects to have high returns from investments in big data, a sound and rational data strategy and big data implementation plan are needed. The data strategies are required to be analyzed regularly. A clear and comprehensive data identification is necessary to minimize the wastage of resources.
3. A company's journey from data analytics to the optimization and improvement of business operations is long. The approach of the pilot project is one way to improvise, gain insight, and resultantly create quick wins. Taking one smart and small step makes the overall position of a company solid and stable. It is a great challenge to determine the direction of movement that the organization follows in terms of data analysis implementation.
4. The combination and integration of data are obstacles for financial companies. The creation of informed analysis for people in the field of finance has not been achieved. Without considering the format and location, the integration of various data sets needs to be made clear. Data analytics is a merger of both IT and business operations. Accountants do not usually understand that, which is a threat to data analytics. Usually, this problem takes place when there is an insufficient understanding of knowledge between finance and IT. Some serious issue arises because there is a dissimilarity between the languages of IT and finance, which results in problems in the interpretation of the analysis. This often occurs because the departments are decentralized. A centralized system where the finance teams work with the departments of operations, the supply chain, and sales and marketing is required for better cooperation and a combination of data analytics.

Data protection challenges of Big Data

There are still some regulatory challenges that have to be tackled to enable the technologies proficient in order to provide competitive and effective solutions.

Data Privacy Protection

Since May 2018, data privacy protection has become a prime requirement in every business about and from the EU countries. Data privacy protection matters have become mandatory, and every company must comply with the General Data Protection Regulation (GDPR). Data privacy issues have not yet been fully implemented in every big data solution. Financial businesses, such as banking and insurance, collect, store, use a large amount of personal data. Regulatory requirements of GDPR dictate that personal data must be collected, stored, processed for specified and lawful purposes only. The impact of GDPR for the banking and insurance sectors is significant because individual clients have the right to ask financial services organizations to remove or refrain from using/processing their personal data in particular situations. The financial services sector may incur an additional cost since they are supposed to deal with the individual request of the clients. The removal of some data may lead to the large dataset being grossly modified/shuffled.

Requirements of Confidentiality

All third-party information that is processed in Big Data analytics is supposed to be confidential in nature. Thus, the financial services sector, including the banks and insurance companies, will have to comply with a mandatory requirement of maintaining data confidentiality so that no data breach occurs.

Conclusion

It is fascinating to notice financial companies have initiated considering big data analysis as a developmental project. Some more prominent companies have already begun to adopt big data analytic techniques. However, the literature review shows that big data is not used in finance, mainly for smaller companies.

Research studies showed that the Big Data method is only advantageous when used with the right set of minds. How the company will enhance its performance is primarily dependent on how the company and its analysts use this critical opportunity. With the usage of automated methods and the right reforms being taken at the right time, along with the integration of big data analysis, the company's revenue can be increased significantly.

Big Data has revolutionized the finance sector in various applications, including real-time stock market analysis, fraud prevention, and machine learning-powered risk analyses. Services can improve customer satisfaction, increase revenues, analyze financial performance, and improve, control, and develop products. Despite several advantages, there remain severe concerns about big data in the field of finance.

The use of Big Data and techniques to analyze it create substantial competitive advantages for the companies that can do that. There is a set of major challenges in the world of finance that are generated by big data. All financial products concerning services are dependent on data and, in turn, also produce data. There are still many areas that need particular research developments in big data.

Ongoing data regulatory compliance is the top priority of the financial sector. Big Data analytics in finance must be customizable to incorporate the requirements of data privacy and confidential protection criteria of GDPR in EU countries.

Further research studies in financial data management systems must be undertaken to determine how the large data sets can be collected/handled and analyzed to devise more efficient financial data solutions.



It is common knowledge that getting a suitable job is a difficult task. On the contrary, recruiting the right candidate for a specific job opening is equally challenging for any striving company. Often recruiting tasks are complex, costly, and demanding. There is also competition among companies to recruit and retain highly qualified and experienced employees.

Big data studies can rescue a company from the complex recruitment hurdles. The impact of big data analytics in the recruiting process is undeniable irrespective of industry and business. In this article, we concentrate on the role of big data analytics in the ever-evolving recruitment processes.

## Big data improves the quality of Recruits

A bad hire is not only a misuse of the company resources, but a bad employee can also spoil a company’s work culture. It is necessary not to mention the loss of resources in useless recruitment processes. Nevertheless, how to reduce errors in recruitments? Big data analytics can help reduce errors in intelligently selecting new hires. Big data studies can help a company go for evidence-based recruitment decisions.

HR professionals and recruiters can select the ideal candidates easily using big data analytics of online databases and publicly available candidates’ information. Identifying the suitable candidates with matching requirements becomes a job of sorting based on key performance metrics of the complete set of candidates. Big data also makes it possible to find more applicants per opening and more effective lead sources.

## Big data can create a larger pool on candidates

In the rarest situation, the recruiting manager instantly finds the “right” candidate to hire. However, in a normal scenario, the recruitment manager requires interviewing more candidates. The most unpleasant problem is not getting enough pool of candidates to interview.

Big data can address this problem by using analytics instruments. Big data can reach a larger pool of candidates for interviews and selection. A bigger pool of talents can help better analyze and optimize the hiring process by addressing skills gaps and future hiring requirements. Big data can help have a line-up of candidates ready to interview, thereby making the hiring process faster.

Big data can improve programs of employee Development and Retention

Recruitment of the right candidate does not end the process to a full stop. The recruitment process goes deep inside the employment and up to training and retention. These follow-up programs should be successful. Otherwise, the whole selection process may end up a waste of time and money.

Big data can contribute to employee training and retention. The HR department can analyze the effectiveness of a training and development program. These studies can also search for ways to improve and simplify the training methods. Studying these data analytics can help craft better employee retention strategies, resulting in increased job satisfaction among the employees.

## Big data can help optimization of hiring

Over-hiring and under-hiring can result in losses in a business. The former creates additional costs incurring direct monetary losses, while the latter can affect/lower productivity causing indirect losses. The recruiters and HR managers can avoid such unfavorable situations by adopting data-driven hiring plans. These plans can continually be updated based on the needs of the company.

The data analytics may also consider the forecasts based on the analysis of various recruitment factors like attrition, lateral movement, promotions, and quality of hire. Big data may also help remove bias in recruitments.

## Big data helps lowering the cost of Recruitment

Big data analytics provide the hiring departments with a complete view of overall performance versus budget. Big data analytics can immensely help streamline recruitment spending by utilizing the complete picture of the recruitment processes. These studies help the hiring managers determine which lead sources have produced the best quality hires, which job ad campaigns have resulted in more applications, and which platforms have attracted which types of candidates.

The top-level managers can utilize all of this information and make data-backed decisions on where to invest money, be it on mentorships or advertising. Finally, big data studies result in cost savings.

## Conclusion

These five points discussed are just the very few among the many other aspects of a complex recruitment process that big data studies can improve.

The hiring managers can improve the candidate experience, recruiting capacity, and speed of hire by utilizing analytics-driven facts and figures, thereby intensifying the employer’s brand image.

The key factors that remain are finding the data, classifying it with data analytics, and deriving relevant insights from the analyzed data.

More data is not always better if not analyzed and inferred correctly. Companies should not go for vanity metrics which are redundant in recruitment processes.

The recruiters have to invest in learning data science and big data analytics. The knowledge of data science and analytics would help adopt the appropriate analytics tools.

There is much opportunity to go for data-driven recruiting, and it would help optimize the whole recruitment process and save considerable time and cost.



People generate massive amounts of data daily, and each year, the amount of data generated grows rapidly. It’s estimated that by 2025 the total amount of data created, captured, copied, and consumed annually will grow to more than[ 180 zettabytes](https://www.statista.com/statistics/871513/worldwide-data-created).

Think of it that way, whenever customers visit your website or your retail store, buy from you, leave feedback, or review your product, join your email list, or get in touch with you in various ways, they leave a trace that can be collected, processed, and analyzed.

On top of that, there is data coming from mobile phones, apps, IoT devices, social media, and even fridges that have various sensors and are connected to the Internet.

But data doesn’t come only from your customers or outside your organization; it is also generated within your company. Calls, emails, marketing campaigns, sales, and CRM systems all create enormous amounts of information that can give you a business advantage if effectively used.

However, globally only a small part of generated data is kept, and even less is analyzed adequately by businesses. To stay competitive in today’s world, your company must be data-driven, yet simple data gathering is not enough. No matter how much data you collect, you only get value from it if you analyze and act on your data.

## What is Big Data analytics?

By Big Data, we mean high volume, high velocity, and highly varied data that is too large and complex for processing by traditional database management tools.

Big Data Analytics, in simple words, is a process of examining big data to uncover information that can help your organization make informed business decisions. Big data analytics uncover hidden patterns and find correlations, market trends, and customer preferences. With big data analytics, you can make better and faster decisions and model and predict future outcomes.

## How Big Data analytics works

Big Data encompasses such a vast amount of information that Big Data Analytics is more of a combination of different technologies and techniques working together than a single do it all solution.

Before your data can be appropriately analyzed, it must be collected, processed, and cleaned.

The data collection process will look different depending on your business model and niche. The type of data affects how it is sourced, collected, and scaled. You can generally collect structured or unstructured data, with the latter being more commonly collected from[ various sources](https://www.ibm.com/cloud/blog/structured-vs-unstructured-data\).).

Structured data is stored in tabular formats (such as Excel sheets or SQL databases) that take less space. It is very scalable since it can be stored in data warehouses.

On the other hand, unstructured data is saved as media files or NoSQL databases, both of which take up more space. It can be stored in data lakes, making scaling difficult.

When data is properly collected and stored, the next step is to organize the data correctly to get actionable results on analytical queries. There’s more than one way to organize data, and the method will depend on the task and your company’s resources.

We classify processing environments into centralized and distributed. Centralized processing happens when all the processing takes place on one computer system, for example, a dedicated host. Distributed processing, on the other hand, happens on multiple systems and is often required with vast datasets as it allows separating large datasets into smaller chunks and processing them in parallel.  

Based on the[ processing](https://www.geeksforgeeks.org/difference-between-batch-processing-and-real-time-processing-system/#:~:text=In%20batch%20processing%20processor%20only,and%20active%20all%20the%20time.\&text=Jobs%20with%20similar%20requirements%20are,the%20computer%20as%20a%20group.) time, there is batch processing and real-time processing.

Batch processing is a slower process where large pieces of data are collected over time and processed in batches. It’s preferred where there is a need for accuracy over speed.  

Real-time processing is used when speed over accuracy is preferred. It’s more complex and costly than batch processing.

The final step before data can be analyzed is data cleansing. This is an important step that improves the data quality and ultimately leads to more accurate results. In short, the process can be described as scrubbing for any errors, duplications, irrelevant data, etc. Any wrong or irrelevant data must be removed or taken into consideration.

### Types of Big Data Analytics

There are four types of data analytics:

### 1. Descriptive Analytics - “What happened?”

These tools tell you what happened by summarizing past data and presenting it in a way that is easy to understand, for example, as simple reports or visualizations.

Descriptive tools seldom incorporate AI and machine learning techniques. A good example would be a sales report that shows you all the sales data from the past few years in a simple graph.

### 2. Diagnostic Analytics – “Why did this happen?”

Diagnostic tools answer why something happened and help you find the root cause of the problem. They are more advanced than Descriptive Analytics and often use techniques like[ data mining](https://www.simplilearn.com/what-is-data-mining-article) but don’t necessarily need not rely on AI and machine learning to be accurate.

Imagine that you are in the SaaS business, losing users. You could use diagnostic analytics to find out why your users are canceling their subscriptions and then develop a solution to counter this problem.

### 3. Predictive Analytics - “What might happen in the future?”

Predictive analytics rely on data mining, AI, and[ machine learning](https://www.simplilearn.com/tutorials/machine-learning-tutorial/what-is-machine-learning) to analyze current and historical data and predict the future. It’s widely used in many different sectors, from retail to health to finance.

In practice, predictive analytics can be used to predict market and customer trends or a soon-to-happen failure in a piece of machinery. It’s also used in credit scoring systems to help approve or deny loans within minutes.

### 4. Prescriptive Analytics - “What should we do next?”

Prescriptive analytics, in short, provides the solution to a particular problem. Based on data and considering all relevant factors, it helps you decide what you should do next.  

Those tools are widely used in business. They help companies make data-driven decisions in sales and marketing, among others. One example would be lead scoring, a process of ranking the sales readiness of a lead.

Prescriptive analytics is also used by some of the biggest companies like Netflix. Netflix uses prescriptive analytics to determine what customers are most likely to watch and when those recommendations should be served.

Benefits and Advantages of Using Big Data Analytics in Business

Big Data analytics helps organizations in many ways. It often has a tremendous impact on sales, customer service,[ marketing](https://www.forbes.com/sites/forbescommunicationscouncil/2022/02/03/market-smarter-how-to-personalize-customer-interactions-with-data-analytics/?sh=58a5be806516), and new product development, to name a few.

Data-driven decisions lead to more efficient operations, higher profits, and happier customers.

Depending on your sector and business model, the benefits of deploying big data analytics vary. Most commonly, businesses use it to:

1\. Drive Innovation

Innovative companies constantly look for new products and ways to improve existing processes and services.

Big Data Analytics helps companies analyze sales data, product performance, and customer feedback, improving existing products or leading to new product development.

Analyzing the collected company’s insights can reduce costs, identify new opportunities for revenue generation, improve efficiency, and lead to new business strategies and marketing techniques that will create a[ competitive advantage](https://sloanreview.mit.edu/projects/analytics-as-a-source-of-business-innovation/).

2\. Accelerate Business Transformation

Big Data analytics is also crucial for implementing operational strategies and facilitating business transformation. Analyzing and acting on your data can help you increase operational profitability and provide your company with new innovation-driven opportunities.

Companies that use big data analytics see vast improvements in many areas, including financial management, product development, and time/cost saving.

According to a recent survey, the COVID pandemic has speeded up organizations’ data-driven strategy, and[ 78% of IT decision-makers agree](https://foundryco.com/tools-for-marketers/research-data-and-analytics/) that collecting and analyzing data will change the way their companies do business in the next 1-3 years.

Again, big data can help you transform your business in different ways depending on your niche and business model. Examples include automating manual processes, thus saving hundreds of hours per year and reducing costs. Or, in the case of retail, learning and analyzing customer habits to create recommendations based on purchase history.

3\. Drive Better Customer Service

Big data can also be used to improve your customer service. Companies commonly use big data analytics to improve customer experience and increase customer retention and royalty.

Analyzing inputs from your website, social media, chatbots, sales, and marketing can help you solve customers’ problems faster or prevent them from happening in the first place. And in some sectors, just a small,[ 5% increase](https://media.bain.com/Images/BB_Prescription_cutting_costs.pdf) in customer retention can result in more than a 25% increase in profit.

4\. Increase Security

Another critical area for big data analytics is[ IT security](https://www.exabeam.com/siem-guide/siem-analytics/). In the past, security incidents were detected by two analytical techniques: correlation rules, network vulnerabilities, and risk assessment. They were good at detecting known bad behavior but suffered from false positives and exposure to zero-day attacks.

Thanks to big data, it is now possible to collect and analyze all internal and external information to bring your IT security to the next level. Big data solutions can identify personnel, network, or device behavior anomalies. Thanks to machine learning, it is possible to stop zero-day malware.



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