Banking analytics is the use of machine learning techniques and artificial intelligence in customer data to make decisions in banking domain. Banking analytics is a management tool that gives insight on current performance and highlights areas where there is scope of improvement. This will help banks make informed decisions, prevent errors, and improve efficiency.
Benefits of Banking Analytics
Upgrading banking and financial services using banking analytics can help them deliver better internal and external services to their customers. Banks are using Big Data Analytics to decipher customer spending patterns, segment out different customers, identify new possibilities for marketing financial products, and lot more.
A 360-degree insight of customers. Leveraging banking analytics, financial services organization can understand customer’s preferences, what motivates them, what matters to them, their overall behavioural pattern. There might be a probability that the sales figure might reflect one of requirement, but the data might speak of another type. We should also go by the data. It also helps in sentiment analysis.
Building strong customer relationship. Providing personalized services, offering the right product at the right time, can reduce churn and improve revenues. A report from Forrester mentions that a single point improvement in financial services organizations’ CX score can improve revenues from $5-$123 million.
Risk management and mitigation. Predictive analytics helps in warding off potential frauds. This is possible because using banking analytics, customer’s behavioural pattern can be traced and any signs of anomaly can be flagged as fraud. Not only for customers, but it also provides protection to the banks from different fraudulent activities. Thus, excusing from reputational damage.
Lower operational cost. In non-analytics aided set-up, banks and financial services organizations are under constant pressure to maintain their targets, profit margins and improve operations. Financial services firms can leverage predicting analytics, visualization, and AI for workflow automation. Replacing paper-based forms with digital applications and increased usage of NLP technologies helps in reducing manual efforts. It also reduces probability of errors.
Competitive advantage. Banking analytics encourages upselling and cross-selling strategies because it enables sales representatives to recommend complementary products and services that will target the correct customers. In emerging markets, banking analytics helps in identification of new business models. New business opportunities can be created, findings can be shared with new partners, thus expanding growth opportunities with the bank.
Developing a Successful Banking Analytics Strategy
Following are some best practices to follow during implementation of the banking analytics strategy:
Trial and Error. Developing a banking analytics strategy is an iterative process. There is no correct formula to it. Every project gives a different idea about the line of work and should be seen as an opportunity to learn and adapt.
Build a data ecosystem. Banks should be open to both internal as well as external data. This is because external data can provide valuable context to internal findings.
Customer insights across multiple data platforms. As mentioned earlier, a 360 degree view of customers provides valuable information. It collects information about a customer from all access points and helps create a holistic image.
Structured process. To properly adept banking analytics, we must:
- Analyze company landscape to understand the metrics and goals
- One should communicate att the changes happening to all the levels to track the impacts.
- Trainings is necessary to update on the protocols.
- Feedback loop should be shared to the employees so that a support system maybe introduced, if required.
Automation. Low-level service requests such as adding someone to an account or adding a new credit card, can be taken cared of via automation. This saves agents’ valuable time, thereby enabling them to focus on more high-level, high-value requests.
End Notes
Banking Analytics is a progressive step for the banking industry. Banks continuously monitor the transaction behaviour of their customers in real-time. Using big data developers, banks can optimize their internal activities and procedures for increased efficiency. They provide the resources that their clientele require.
