What is banking analytics?
Banking analytics describes the process of collecting information pertaining to the field of finance and analyzing it to obtain insights and actionable recommendations. By distinguishing patterns in up-to-date market trends and customer behaviors, fintech companies and banks can transform their operations and significantly improve their service.
Modern-day data analytics in banking relies on already familiar techniques, such as data visualization, but with artificial intelligence gaining traction, banks can also leverage big data to direct their decision-making.
Why is banking analytics important?
Without big data analytics in the banking industry, there is a high chance for companies to struggle to catch up to their competitors, since they would be relying on outdated information. On the other hand, businesses that choose the data-driven approach benefit from sounder risk management, in-depth customer knowledge, and optimized operating expenses.
How can banks benefit from using data analytics?
Based on the great analytical capacity that technologies like AI offer, there are several key benefits of data analytics in banking:
- Better risk management: Bank analytics makes it possible for banks to detect fraud at more reliable rates, strengthen the safety of customers’ assets, and safeguard themselves against losses.
- Guided decision-making: Knowing what your customers want is paramount. Banks can benefit from accurately determining what improves customers’ loyalty, analyzing reasons for churn, and finding better personalization opportunities.
- Cost saving: Another use of data analytics in banking is to streamline operations. AI, for instance, works beyond a descriptive function — it can also generate tailored optimization recommendations and assess the results after their application.
- Regulatory compliance: Adhering to strict requirements in the financial field is a must. Banking data analytics makes it easier for companies to comply with the necessary regulations, taking into consideration such aspects as the area a business operates in.
How is data analytics used in banking?
The described benefits are best demonstrated by data analytics use cases in banking. Here are several examples:
- Improved security: Big data analytics in banking makes it possible for institutions to dissect the smallest details about transactions and detect abnormalities, such as a strange IP or failed login attempts. First of all, it helps flag potential scams and money laundering. Additionally, ML algorithms can analyze a person’s credit score, income, and debt history to guide creditworthiness decisions.
- Proactive equipment maintenance: Downtime and equipment wear can result in significant expenses if not addressed on time. Banking analytics solutions developed for this purpose keep track of the condition of the company’s equipment (such as ATMs or security infrastructures). It can also predict maintenance needs ahead of time.
- Personalized marketing: Analytics in banking and financial services presents targeting and customer segmentation as two of its main strengths. It describes the demographics, behavioral patterns, and preferences of customers, thus presenting banks with an opportunity to develop more successful marketing strategies.
- Optimized resource allocation: This case of data analytics in the banking sector is particularly relevant to banks with multiple branches. For such institutions, allocating resources efficiently can be a difficult task. Analytics can help decide on staffing needs and resource allocation, depending on each of the branch’s traffic and performance. It can also be of use during expansion endeavors.