Big data is a term for enormous and intricate data collection. Businesses gather, examine, and aggregate this data to gain unique insights and make informed decisions.
It includes an exponentially growing mix of unstructured, semi-structured, and structured data. This data is exceedingly challenging for traditional data processing systems to handle.
In fact, it’s not only defined by the amount of information growing at a pace that exceeds the capacity of current databases. It is also characterised by its intricacy, variety, and the speed at which the data must be analysed.
The financial services industry stands to benefit greatly from the utilisation of big data. These benefits include enhanced competitiveness, reduced expenses, opportunity conversion, and real-time risk mitigation.
This article particularly explores big data in banking and its role in advancing risk management and fraud detection.
How are banks leveraging data?
Banks are largely leveraging data to improve client experiences, streamline operations, and inform strategic decision-making. For example, banks are utilising artificial intelligence (AI) to analyse massive volumes of customer data. This includes transaction histories and behavioural patterns to understand customers’ financial habits.
In addition, more banks are embracing an increasingly data-driven culture to manage the challenges of digital transformation. Furthermore, the competitive environment and regulatory requirements are pushing banks to optimise their internal data management procedures.
What are the benefits of data analytics in banking
According to Markets and Markets, the global big data market size is projected to reach $229.4 billion by 2025. This expected growth highlights specific benefits of data analytics for the banking industry, such as:
Gain a complete view of customers
Thanks to data analytics, banks can compile client data from multiple sources. This data includes transaction history, online activity, and demographics. By aggregating this information, banks can gain a holistic understanding of customer demands, tastes, and behaviours.
With this knowledge, they can uniquely target services to individual customers and segment their clientele. This approach helps build stronger client relationships.
Personalised customer experience
Data analytics can enable banks to curate personalised experiences for their customers. For example, offer customised product recommendations to specific customers based on individual spending habits or financial goals.
This level of personalisation enhances customer loyalty. It also appreciably increases the likelihood of cross-selling and upselling financial products.
Competitive advantage
Data analytics gives banks a major advantage in a highly competitive financial industry that has exciting FinTechs emerging. Banks that exploit data analytics intentionally can spot new market opportunities and trends before their rivals do.
Consequently, they can quickly innovate and modify their services to stay relevant and customer-friendly. Additionally, more strategic and proactive resource allocation and investment can result from leveraging data analytics.
Reduce the risk of fraudulent behaviour
Banks can use data analytics to identify irregularities programmatically. By examining transaction patterns and client behaviour, these irregularities might indicate fraud. In fact, sophisticated algorithms and machine learning models can continuously monitor transactions in real-time.
They can also flag questionable activity for additional inquiry. This proactive adoption of data analytics not only safeguards a bank’s assets but also improves client security and trust.
Increased efficiency of manual processes
Data analytics can automate and streamline several manual operations in banks, increasing operational effectiveness. Data-driven insights, for instance, can streamline the loan approval process by automating risk and credit assessments.
Additionally, by providing precise and timely data, analytics can minimise the need for significant manual intervention. This improvement enhances reporting and compliance operations.
This efficiency saves time and money and frees up bank staff members. They can then concentrate on higher-value duties that enhance client satisfaction and drive company expansion.
Big data in banking applications
Big data has seen several application use cases that have disrupted the banking sector. Some examples are:
Predict financial trends
Some banks are using machine learning algorithms. They develop predictions about future market movements and modify their investment strategies. This capacity helps banks manage risks and optimise their portfolios, in addition to improving decision-making.
Furthermore, big data analytics is facilitating the development of more precise financial models. These models help banks predict market changes, spot possible investment opportunities, and reduce the risks connected to unstable financial environments.
Examine pre-existing risks
Big data is being explored to identify fraud and terrorist activities among its bank employees. Some international banks possess the capacity to process vast amounts of data to pinpoint individual behaviour patterns and reveal potential risks via real-time machine learning.
As a result, banks can spot incorrect or unusual charges and other suspicious employee transactions.
Automate key processes
Banks can lower risks and save significant amounts of money by using advanced automation powered by big data. This approach removes human intervention from some crucial processes.
Among the early adopters of automation in the banking services sector is JP Morgan Chase & Co. Currently, the business uses a number of A.I. and machine learning tools to streamline some of its operations. For example, algorithmic trading and the interpretation of commercial loan agreements.
One of its products, LOXM, uses historical data gathered from billions of transactions to allow stock trades to be made at maximum speed and optimal prices. This automation has proven far more effective than previous automated and manual trading methods, thus saving the bank a substantial amount of money.
Customer profiling
According to McKinsey, companies can save up to 15-20% of their marketing spend by leveraging data to inform their decisions. Considering that banks spend 8% of their total budgets on marketing, utilising big data seems like a no-brainer. It can minimise costs and increase income through highly focused marketing campaigns.
For instance, Barclays has been utilising sentiment analysis in a unique way. Dubbed “social listening,” the bank extracts valuable information from user behaviour on social media platforms.
When the company first released its mobile app, users under the age of 18 could neither send nor receive money. This restriction infuriated many people. Customers who were not happy responded by venting their frustration on social media.
Barclays resolved the issue by granting users 16 years of age and older access to the full functionality of the app. This happened as soon as the data it had gathered made it clear what was wrong.
Detection and prevention of fraud
According to a survey released by LexisNexis Risk Solutions, every $1 lost to fraud now costs $4.36 in associated costs, such as legal fees and recovery. This means that fighting fraud is becoming more expensive for U.S. banks.
In 2023, fraud cost the world’s banking industry close to $500 billion. This included $450 billion in fraudulent payments. It also included over $40 billion in schemes targeting businesses and individuals, and check and credit card fraud.
Big data analytics are becoming a crucial component of any strategy to assist in identifying and curbing financial crime. Criminals are constantly changing their methods of breaching technological systems by taking advantage of multichannel weaknesses. Big data has made it possible for banks to use real-time analytics in response to these escalating risks.
For instance, machine learning systems are being designed to operate at a rapid pace on massive data sets. They find hidden patterns in the data while learning independently to compile information about dubious transactions.
Accurate risk management
Credit risk management is important in banking. It is the practice of mitigating losses by understanding the adequacy of bank capital and loan loss reserves.
Traditionally, banks work with other financial organisations to store, examine, and assess a client’s credit history to determine the client’s ability to repay debts. By leveraging big data, banks can now assess customer financial behaviour more effectively.
They can independently make lending judgments based on various factors. These factors range from specific transactions to credit reports, spending patterns, and loan applicant payback rates.
Some banks have gone one step further and programmatically consider the client’s reputation in its entirety. They utilise resources like Amazon, eBay, and Facebook to gain a deeper understanding of possible borrowers’ financial habits and character traits.
This strategy’s underlying premise is that conventional assessment techniques are unable to reliably gauge an individual’s creditworthiness.
Big data in investment banking
Investment banks are exploiting big data in the form of predictive analytics to improve strategy formulation and decision-making.
These banks can analyse market trends, client habits, and economic indicators. They use large amounts of both organised and unstructured data to estimate future financial scenarios.
For instance, Goldman Sachs automates intricate market evaluations, improving its ability to recognise investment opportunities and hazards. With this capability, it can create data-driven investment plans that iteratively adjust to shifting market dynamics and eventually gain a competitive advantage.
How can big data benefit large-scale trading banks?
AI and advanced analytics are driving a major revolution in the rapidly changing large-scale trading bank sector. They are improving their capacity to evaluate massive volumes of data and make strategic decisions.
Trading banks can currently gain insights into risk variables and client behaviours and create trading methods that improve their portfolios. For example, these banks can discover new patterns by analysing real-time data from multiple sources, such as market transactions and economic indicators.
This ability allows banks to react quickly to market movements and increases forecast accuracy, giving them a competitive advantage in the cutthroat financial sector.
Big data in banking case study
To enhance Anti-Money Laundering (AML) compliance, a central bank faced challenges with traditional methods as financial crime grew. High-risk money changers exploited system vulnerabilities, making detection difficult.
In response, the bank implemented an advanced analytics platform that analysed transaction patterns, customer profiles, CCTV footage, and other data sources to identify potential risks.
The results were impressive: a 30% reduction in high-risk companies, improved detection, leading to more successful investigations, and enhanced AML compliance.
This approach strengthened the protection of the financial system and boosted the efficiency of the supervision team.
Read more here.
Conclusion: What is the future of analytics in banking?
The banking industry has never been one to adopt new technologies quickly. However, by ignoring big data, many banks risk being left out. In essence, big data is the key to unleashing marketing potential in the fiercely competitive financial market of today.
However, the banking industry still has unrealised potential for value creation. Financial service organisations are still lagging behind in using big data analysis tools.
Numerous issues contribute to this underutilisation. These issues include data silos, legacy systems, and a shortage of staff with the necessary skills.
Innovative financial organisations known as challenger banks, like Monzo in the U.K., are great examples of disruptors who are willing to exploit big data. In practice, Monzo functions mostly through digital platforms and lacks traditional physical branches.
Monzo Bank employs Google BigQuery to manage and analyse vast amounts of data generated from its operations. This ability enables Monzo to streamline its processes and appreciably reduce in-app support requests.
It also empowers non-technical staff to answer 85% of business intelligence queries independently. This big data adoption enables them to challenge the established banks that have been slower to adopt such technologies.
So, to remain relevant, banks must invest in advanced analytics capabilities and foster a culture that prioritises big data utilisation. In the end, the banking industry is at a turning point in its history, and how it chooses to go will determine its place in the digital era.
