Big Data has cemented its place as a critical component for improved decision-making. The banking and financial industry is possibly at the forefront of leveraging the power of big data to alleviate some of the critical challenges.
The big data analytics market was valued at USD 29.87 billion in 2019 and is expected to reach USD 62.10 billion by 2025.
Banking and financial services industries are highly regulated industries. While the regulations are essential, they have also been impediments to fast and customer-oriented service. Companies need the tools to process vast sets of structured and unstructured data. Only through such data analysis, they can expand their services, meet the growing demands of specific segments, and also expand their global footprint.
The data challenge – processing structured and unstructured data
The banking and financial services industry is inundated with data. Digital transformation is becoming a priority in this sector. With the rise of the internet and smartphones and with customer interactions increasingly moving online, the number of electronic records is only growing.
The banking and financial services industry can leverage this data to derive crucial business insights, improve scalability, and improve their decision-making processes.
This data at the disposal of most of these companies is both structured and unstructured. While structured data is important, it is equally essential to have the capability to collect, process, and analyze unstructured data.
Research shows that almost 80% of the data in this industry is unstructured.
This data is mostly underutilized, and 75% of organizations feel that having the capability to combine structured and unstructured data would provide immense value. Internal reporting, internal communication (e.g., email), communication with clients, social media data, etc. constitute the vast bulk of unstructured data.
Extracting value from unstructured data needs elevated analytics capabilities to drive positive business outcomes.
Fraud detection and risk management
Big data analytics has given banking and financial services companies the power to improve their fraud detection capabilities. By applying big data analytics, companies can differentiate between normal activity based on a customer’s history and highlight unusual behavior that could indicate fraud. This also gives them the ability to respond to fraud faster as fraud insights allow them to take action in real-time. It also facilitates collaborative and sophisticated case management by performing and managing inquiries into any suspicious activity.
Big data analytics also assists in identifying potential risks associated with money lending processes in banks. Leveraging data insights, banks can analyze market trends and make informed decisions on increasing or lowering interest rates. They can also identify customers with poor credit scores and improve their risk management capabilities.
Getting a 360-degree view of the customer
The customers of the banking and financial services sector use multiple channels – web, mobile, social media, IVR, etc. The sheer number of products, the volume of transactions, and the locations make it hard to map customer interactions.
With pervasive data silos, it becomes hard to gain a 360-degree view of the customer. As a result, it can be a challenge to improve customer retention, improve customer experience, ensure customer loyalty, and provide customers with a high degree of personalization.
This industry needs seamless data integration capabilities and the power to process and analyze both structured and unstructured data from multiple data sources to personalize solutions and marketing initiatives, identify customer spending patterns, and enable better customer segmentation.
Big data analytics has given banking and financial services companies the predictive power to improve their risk models and improve system response times and effectiveness.
This technology has been a great enabler of automated processes and more predictive systems to improve risk intelligence from various data sources in real time. Big data analytics enables companies to drive intelligent processes for error proofing and to automate exception handling. It assists business process management and analysis and helps augment and aid existing BPM capabilities.
Along with these capabilities, data analytics is also helping organizations navigate the complex regulatory landscape and lower the cost of compliance reporting. It helps them come up with new ways to manage risks with insights derived from data and bring about improvements in core operations to spread efficiencies across business lines.
As competition in the banking and financial services industry gets fiercer, it becomes clear that this industry has to develop the capabilities to use the huge volume of data at its disposal and convert it into meaningful insights.
Leveraging big data analytics to become a data-driven organization is now a business imperative, and perhaps the only way to stay ahead of the curve.