The digitisation of the modern world has been rapidly accelerating over the past few decades, resulting in an enormous amount of data that is predicted to reach 175 ZB (zettabytes) by 2025. This has given rise to big data, which has become a critical asset for businesses across a range of industries. 

The financial industry is one of the most data-intensive sectors out there, offering an incredible opportunity to process, analyse, and leverage the data in meaningful ways. At the same time, the industry is also highly susceptible to risks such as financial crimes, credit risks, market volatility, etc. Therefore, the use of big data in financial risk management has become essential as it is a powerful tool that enables businesses to anticipate and mitigate potential risks before they occur. By making use of big data analytics and risk management tools, businesses can gain valuable insights into their operations, identify potential threats, and take proactive measures to mitigate risks. As a matter of fact, a report published by McKinsey Global Institute states that data-driven companies are 19 times more likely to be profitable. 

How is big data revolutionising the financial industry?

First and foremost, what is big data? 

Big data is a term that refers to extremely large datasets that are too complex for humans to manage effectively. Big data is collected from various sources, including social media, sensors, machines, and other digital devices. It can come in the form of structured, unstructured, and semi-structured datasets. The amount of big data generated in the finance industry is growing exponentially, transforming and revolutionising the way financial institutions operate. The sheer volume and complexity of data generated have created new opportunities for companies to derive insights, manage risks effectively, and make better-informed decisions. 

How does big data help manage risk?

Traditionally, bank employees were responsible for number crunching, and business leaders made decisions based on manually calculated risks and trends. Big data analytics, however, presents an exciting opportunity for banks and financial institutions to tackle and mitigate risks. 

By leveraging big data, financial institutions gain access to a vast amount of data from multiple sources, including customer transaction histories, social media activities, and sensor data from physical assets. With the right tools and technology, financial institutions can analyse this data to identify patterns and trends, which can then inform risk management decisions. For instance, if a financial institution notices an increase in delinquent loans in a certain geographic area, they can investigate further and take appropriate measures to manage the risk.

Besides that, by analysing patterns and trends in data, financial institutions will be able to gain a comprehensive understanding of risk across various areas of the business. This includes risks related to credit, market, operational, and liquidity. When coupled with Artificial Intelligence (AI) and Machine Learning (ML), financial institutions will be able to take the analysis to the next level. The traditional data analysis methods in managing risks have limitations — they may not capture all the relevant data or analyse it in a timely manner. Additionally, manual data analysis and monotony are prone to human errors. In fact, the greater the monotony, the higher the error rate in most situations. 

However, AI and ML enables financial institutions to uncover patterns and correlations that humans may not be able to see, and the algorithms make it possible to do so at a speed and scale beyond human capacity. Furthermore, its prediction accuracy increases over time when more data are being fed. For instance, AI and ML can identify links between seemingly unrelated data points, such as social media activity and credit risk. If all data points to a loan applicant being at high risk for default, financial institutions can take proactive measures to manage and mitigate the credit risk before it becomes a problem.

What are some major challenges associated with big data?

While the idea of implementing big data can provide significant benefits, it also comes with its own set of challenges. 

Data quality

Bad or outdated data can lead to inaccurate predictions and analysis, which can cause poor results in decision-making. Data quality issues can arise from various sources, such as human error, technical glitches, or data entry issues. Therefore, it is critical for organisations to have robust data quality processes in place to ensure that the data used for analysis is accurate and up-to-date.

Data integration

Like most businesses, banks and financial institutions often use multiple systems to store and manage their data, resulting in data silos. These silos make it difficult to get a comprehensive view of all the data, which can hinder efficient analysis. To effectively leverage big data, businesses need to integrate data from various sources into one centralised platform. If done manually, this process can be complex and time-consuming, requiring careful planning and execution to ensure that the data is properly aligned and structured. Failing to do so may negatively impact business operations such as inaccurate predictions and flawed decision-making. 

To address these challenges, we created Juris DecisionCraft, an automated decision engine that orchestrates and synergises data and analytics from different sources to achieve the best result. It is designed to empower business executives and decision makers to make fast data-backed decisions at scale. 

Understanding data’s decision

Another key challenge in implementing big data for organisations is the issue of black-boxed data decisions. As machine learning algorithms become more sophisticated and capable of making decisions based on data, it can become difficult to understand how those decisions were made. This could pose a problem for financial institutions that need to be able to explain their decision-making process to stakeholders and/or regulatory bodies. In some cases, the decisions made by machine learning algorithms may even be in violation of laws or ethical standards without anyone realising it. Therefore, it is important for organisations to be transparent about their data decision-making process and to ensure that their algorithms are programmed to prioritise ethical and legal considerations. 

This is where Juris Mindcraft succeeds —  it is an explainable AI, which means it can provide an explanation behind every decision reached. An example of what Juris Mindcraft is capable of is using and providing statistics or past data to support the reason behind prediction.

JurisTech, Your Preferred Partner

The ultimate business goal of using big data in the financial services industry is to gain real-time insight from the data to drive business operations forward. No doubt, the ability to effectively collect, manage, and analyse vast amounts of data will be a game changer for many businesses. But if it is not managed well, it may have the opposite effect, rendering data meaningless. 

If you are looking to take advantage of the power of big data in the financial industry, contact us today for more information. Our team of experts will work with you to understand your unique needs and provide you with solutions that meet your specific requirements. We are passionate about helping our clients in their digital transformation journey, and we have the expertise and tools to help you succeed!

About JurisTech

JurisTech (Juris Technologies) is a leading Malaysian-based fintech company, specialising in enterprise-class software solutions for banks, financial institutions, and telecommunications companies in Malaysia, Southeast Asia, and beyond.

As one of the Fintech pioneers in Malaysia, our vision is to enable financial inclusion for the financial industry with our diverse range of solutions. Check out our latest AI-powered technology Juris Mindcraft, an explainable and adaptive AI that provides an explanation behind every decision reached, which helps banks and financial institutions to transform their digital landscape. 

If you are interested in taking your business strategies to the next level with data-driven decisions, consider Juris DecisionCraft, an automated decision engine that orchestrates and synergises data and analytics from different systems or sources to achieve the best result.