Big Data: Efficiencies, Pitfalls, and Challenges in Financial Services


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Big Data has become an integral part of financial services. It helps brands offer convenient, personalized, and secure solutions to customers. However, despite the advantages, there are challenges to using it too.

Big data helps banks effectively collect, analyze, and use large and complex datasets. This way, banks can offer tailored services, develop risk assessment models, and make data-driven decisions to optimize financial processes.

With economic sectors generating vast data, it helps banks integrate data efficiently and offer robust services with reduced operational costs.

Efficiencies of Big Data in Financial Services

1. Better Banking Solutions and Customer Segmentation

Data analytics has changed the way banks interact with customers.

Integrating data with analytics tools helps banks understand individual customers’ preferences.

Banks can create personalized offerings and services based on their spending habits and personal or economic backgrounds. It enables banks to provide tailored services, resulting in better customer relationships.

Similarly, robust customer segmentation allows banks to properly target customers with suitable marketing campaigns to meet their expectations.

2. Customer Feedback Analysis

The ability to store and process huge amounts of Data allows banks to collate more customer feedback. A continuous study and analytics of this  data offers banks vital insights from customer queries and feedback. These insights help banks respond to customer needs promptly, boosting customer loyalty.

3. Fraud Prevention and Detection

Fraud detection and prevention of suspicious transactions are critical issues in banking. with data playing a critical role in customer identification,  no unofficial or suspicious transactions can be processed. This ensures safety and security.

4. Ensures Compliance 

With analytics and business intelligence (BI) tools, maintaining records and complying with regulations becomes easy. These tools can effectively manage and track all regulatory policies. There are much fewer chances of banks missing their compliance processes or meeting regulations if their data is well analyzed.

Also, when devices are configured correctly, they can help banks manage compliance and reduce errors caused by human intervention.

Challenges with Big Data in Financial Services

Many unstructured and structured sources are actively generating large volumes of data today. Legacy and outdated data systems cannot tackle this disparate data’s vast volume and velocity.

While banks rely on building the right processes, enabling robust tools to extract insights from the data is necessary.

Banks must understand how to manage the big Data warehouses they acquire from their customers. They must align modern tools to overcome the basic resistance to the emerging technologies that will add value to the processes.

Here are a few challenges of using big data as a foundational tool in banking services:

1. Legacy Systems and Lower Data Maturity Levels

Many banks rely on traditional mainframes for their operations. They rely on inflexible IT infrastructures for data processes and sometimes use several legacy systems. These systems cannot compute large data sets. The entire system’s stability is at risk when a vast amount of data is collected and stored in an outdated infrastructure.

Data maturity is a standard to which banks need to adhere to get the most valuable insights. This necessitates a refined approach to analyzing the data that decides the data maturity of the banks.

2. Unstructured Data

With vast data volume, banks often need help to separate valuable data. While potentially useful data is also present, banks cannot sort useful from worthless data. They need to do this exercise for data to deliver any value.

So, structuring data is necessary. Banks can only use effective methods and apps to analyze vast data volumes.

3. Data Quality and Silos

Data is sourced from numerous systems. Due to disparate sources, it may not fulfill the data governance requirements. Data management solutions must be deployed to ensure accurate and secure data.

Also, employee documents, emails, and company apps offer vital financial data. Data integration tools combine and reconcile big data to simplify the storage process.

Big data and the cloud together can tackle and resolve these challenges. As more banks use cloud solutions, they indicate to the financial market that big data solutions benefit financial business applications.

4. Data Security and Privacy

Data regulation is not defined clearly in many countries, hampering its  effective adoption. This makes it hard for banks to understand the legally acceptable process when using customer data. These factors increase privacy and security concerns.

5. Lack of Expertise

Banks are aware of the  opportunities it offers. But, they lack the expertise to close the gap between the data and the possible opportunities.

Lack of skills to work on this technology is a huge challenge most banks face.

The market is facing a severe shortage of skilled data scientists, and unless this is sorted, it is a challenge for every sector. Banks cannot leverage their  data without specific skill sets.

6. Data Complexities

The data’s complexity is a key issue. Scalability, noise, and measurement errors are some of the main problems. Data is collected through various activities with distinct goals. Specifying how filters should be applied is critical so that no crucial data is lost.

While vast data is produced from diverse sources- missing data remains a major concern. This is a high possibility because of the complex nature of data sets. This complexity may also create low-quality data, which is not dependable for insights.

Also Read: How Big Data is Changing the Way Banking and Financial Organizations Operate

Influence of Big Data Analytics in the Financial Sector

The customer information in big data influences financial services‘ production and success factors. Financial analysts can make informed investment decisions and monitor spending patterns using data-based predictive models. Additionally, it has enabled banks to overcome roadblocks, improve the banking experience, and meet customer expectations.

Banks have adopted big data analytics to maintain a solid competitive edge. Statistical techniques will increase accuracy as financial services move towards big data and automation. Moreover, they need appropriate tools to monetize the data.

As per a recent report by Research and Markets, “Big Data: Global Market Size, Share and Growth,”

Big Data: Global Market Size, Share and Growth

Banks will continue to move towards adopting big data solutions due to their untapped potential in financial services.

Apoorva Kasam
Apoorva Kasam
Apoorva Kasam is a Global News Correspondent with TalkCMO. She has done her master's in Bioinformatics and has 18 months of experience in clinical and preclinical data management. She is a content-writing enthusiast, and this is her first stint writing articles on business technology. She specializes in marketing technology, data-driven marketing. Her ideal and digestible writing style displays the current trends, efficiencies, challenges, and relevant mitigation strategies businesses can look forward to. She is looking forward to exploring more technology insights in-depth.


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