In the competitive world of banking and finance, gaining a competitive advantage is vital. This leads to the industry being among the first to adopt new, emerging technology. With access to large amounts of high-quality data, banks have begun implementing predictive analytics to gain an advantage, and it is only set to grow until the adoption is all but a necessity to stay competitive. The Global Predictive Analytics in Banking Market is projected to surpass USD 10.07 billion by 2030 from USD 1.71 billion in 2020 at a CAGR of 19.42%.
Predictive analytics is a branch of data analytics, which uses data to train machine learning models to predict future outcomes. The applications in banking and finance are endless, from predicting customer behavior to forecasting market trends to accurate assessments of risks. Predictive analytics gifts banks and financial institutions the capability to make decisions based on data and gain an edge over the competition.
There are a number of components that are essential in ensuring the implementation of predictive analytics is effective, and results are accurate and reliable.
Predictive analytics begins with the collection of data. Data collection is the foundational step in predictive analytics, as the quality and quantity of the data gathered are the bedrock upon which accurate results are built. For banks, data collection is underpinned by regulatory compliance and industry best practices, due to sensitive data like customer profile information and transaction records can be used.
As mentioned, data quality is vital for getting high-quality, reliable results from predictive analytics. Data wrangling is the process of cleaning and structuring raw data to ensure its suitability for predictive modeling. This includes structuring data into a consistent format and handling missing values or outliers which can skew results. Ensuring data quality is non-negotiable, as any inaccuracies or inconsistencies can lead to unreliable predictions.
Model building is the process of training models using machine learning algorithms, which can recognize customer patterns, financial relationships, and market trends. Algorithms must be chosen appropriately depending on the type of problem. A regression problem, for example, predicting the likelihood of a customer churning, has a set of various algorithms that use different methods to arrive at different predictions. The same is true for classification problems. Choosing the optimal algorithm and fine-tuning these models is an essential step to maximize their effectiveness.
The banking and finance industry requires rigorous scrutiny to ensure that predictive models meet high standards in terms of reliability and performance. Metrics such as accuracy, precision, recall, and F1-score are often used to assess a model's ability to predict financial trends or customer behavior. Cross-validation techniques are employed to validate the models' robustness. Ensuring that predictive analytics systems are performing optimally is paramount, given the significant financial implications of decisions driven by these models.
With that context, it's important to discuss the specific use cases that predictive analytics is already being used in throughout the banking industry.
Read more on use cases across other industries.
Interest payments are a significant source of revenue for banks, however, there are inherent risks associated with them. Delinquency status is assigned to someone who cannot fulfill their payment obligation, and predictive analytics allows banks to accurately and efficiently predict the probability that an applicant will have this status, and possibly default on their loan. With these predictions, banks minimize the risk of bad debt according to their own risk tolerance and have confidence in their loan approval process.
Read further on loan delinquency prediction in banking.
Fraudulent transactions are a growing issue for banks, as identifying them requires a large investment of resources to detect and evaluate. This prevalence of online transactions has fueled an increase in fraudulent transactions, by 2025, there could be a worldwide loss of close to USD 44 billion due to fraud. Predictive analytics provides a proactive solution to prevent losses due to fraud, by identifying anomalies in real time.
Read further on fraud detection in banking.
Customers are vitally important to any bank, generating revenue through transactions, fees, and interest payments. However, it has never been easier for consumers to close their accounts and switch to a competitor, meaning retaining customers is more important than ever. Predictive analytics provides a method to analyze customer behavior and transaction history and predict customers at risk of churning, allowing retention teams to proactively intervene.
Read further on customer churn prediction in banking.
The benefits of implementing predictive analytics usually depend on the specific use case, however, there are a few universal benefits that usually apply.
Many of the predictive analytics use cases in banking are directly related to cost reduction. This can take the form of saving on costs through reduced fraud, lower loan default rates, or extending the value of a customer that would have otherwise churned.
Predictive modeling allows banks and financial institutions to make more informed decisions. Data-driven decision-making provides greater confidence that the optimal decision has been made, with an accurate representation of the risks involved.
With customer churn among banks on the rise, customer experience is vital in maintaining customer satisfaction and loyalty. Predictive analytics provides a more advanced way to segment customers, offering an opportunity for more personalized communication and support which 78% of retail banking consumers expect, yet 44% feel they are getting. In addition to communication, there is also an opportunity to provide personalized product recommendations.
It’s without doubt that implementing predictive analytics is a huge advantage for any bank or financial institution, however, there are industry-specific hurdles that they must be conscious of.
As mentioned earlier, the success of a predictive analytics project is highly dependent on the quality of data that is used to train models. Banks face the challenge of ensuring that their data powering predictive analytics is accurate, complete, and consistent in structure, or risk the resulting predictions becoming unreliable.
With banks and financial institutions using predictive analytics to make decisions, consumers demand justifications for their decisions that are made from predictions, and regulatory bodies such as the SEC and FINRA require compliance with financial regulations. Predictive models developed must be explainable, with results that can be understood and validated. In practice, this means models can’t be a “black box”.
Despite the challenges, the future of predictive analytics in the banking and finance industry is promising. Here are some key trends in the space to watch out for in the future:
Predictive analytics will become accessible to professionals without coding or even data science expertise. The rise of no-code tools such as the AI & Analytics Engine will enable them to build their own predictive models.
Predictive analytics will be performed in real-time. As new data comes in, machine learning pipelines automatically generate predictions, providing banks an advantage with speed in making decisions.
Predictive analytics will enable banks and financial services to provide personalized services such as tailored product recommendations, investment advice, and financial planning.
There will be a growing emphasis on ethical AI practices. Fairness and transparency in predictive models will be essential to maintain trust and regulatory compliance.
Hopefully this blog has helped your understanding of how predictive analytics is transforming the banking industry. If you’re curious about how it’s being used in other industries, check out our other blogs on:
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