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Predictive Analytics in Banking – Use Cases, Metrics, Examples and More

Introduction

The growing challenges in the banking sector such as heavy regulations, bulky transaction volumes, evolving customer needs, and a high-tech financial crime has developed the need for predictive banking analytics. Thus, predictive banking analytics refers to the AI and ML-based technology to gather and process big data sets and identify trends and patterns in the banking industry. As a result, the adoption of the prominent tool provides deep insights into the banking strategies, potential, and possibilities. Moreover, helps to make effective customer-oriented decisions and leverage higher customer satisfaction. Predictive analytics in the banking industry focuses on four major components-Reporting, Descriptive, Predictive, and Prescriptive analytics making it the full-fledged analytics process.

Use Cases of Predictive Banking Analytics

The following use cases of predictive analytics help to draw a healthy balance between customers, employees, and banking authorities.

Use CaseDescription
Risk AnalysisUsing the analytical tools banks are able to build portfolios of the red-flagged clients who have defaulted in making regular payments. The AI-based risk prediction helps to determine the right strategy and hedge-related risk, either by offering a new payment schedule or increasing the interest rate. In short, regulatory reforms based on predictive analytics are the key driver to mitigate potential risk.
Credit ScoringEmploying the statistical machine learning technique on big data helps to boil data into a single value called a credit score. The credit score represents the propensity of the client to make default or payment. As a result, it helps to determine the credit worthiness of the customer and facilitate the loaning process, determine the interest rate and duration of the credit.
Fraud Detection and PreventionBesides, the fast and convenient form of banking there exist the darker side to the real-time transaction structure too. Online fraudulent activities like phishing, identity fraud, card skimming, etc. are seeking the antifraud solutions. Thus, predictive banking analytics emerged as the effective tool to detect the fraud pattern using its Artificial Intelligence model and prevent the potential risk factor in the banking.
Investment PlanningSince, saving, investment and expenditure decisions of the individual are based on patterns; therefore it is crucial for the banks to understand such patterns. So, the concept of predictive analytics came into picture and helps learn patterns using its Machine learning models. Furthermore, it helps to implement client segmentation techniques in the operational process of the banks.

Metrics for Predictive Banking Analytics

The key metrics in the banking operation revolves around defining quantitative value to achieve long term goals and objectives. Thus, the right metric based on Artificial Intelligence framework acts as the speedometer of the organization. Furthermore, it helps to achieve improved productivity of the banking staff, customer retention, facilitate cross selling and certainly the reduced operation cost. Generally, banks determine, identify, and draw prediction on the basis of some common ratios.

Common RatioUsage in Analysis
P/E (Price to Earning) Ratio
= Share market price divided by Earnings Per share
The higher P/E ratio indicates the high growth, low risk, and enhanced payouts for the bank.
Efficiency Ratio
= Banks’ expenses divided by Total Revenue
Here, expenses exclude the interest expenses. It determines the efficiency of the bank to utilize its asset for revenue generation. Lower the ratio better is than the performance. Thus, an ideal efficiency ratio is considered lower than 50%.
P/B (Price to Book value) Ratio
= Market Price divided by book value of the share
The return on equity largely impacts the P/B ratio and indicates higher expected earnings with low-risk profiles.
Capital Ratio
= Banks’ capital divided by the risk –weighted assets.
It focuses on determining the bank’s vulnerability to unexpected events like an increase in bad loans or debts.
Loan-to-deposit Ratio
= Banks’ total loans divided by total deposits
It refers to the bank’s liquidity rate. The extreme higher ratio reflects that bank’s susceptibility to a bank run whereas the too low indicates the bank’s underutilized earning potential. Thus, it is necessary to have moderate relation between the loan and deposits.

Examples for the Predictive Banking Analytics

The widespread use of predictive analytics in the banking sector has resulted in the healthy revolutionary phase. Few prominent examples where the technically potent analysis tool has shown significant outcomes are as follows.

ExampleHow Predictive Analytics is Applied?
Citi bank –Customer AnalyticsA renowned bank has emerged as the flag bearer of data-led analytical tools where the major focus was on customer acquisition and retention. Using the Machine Learning algorithms Citi bank targeted promotional spending. Reportedly, the bank has also made the best use of cloud-based payment receivables to organize corporate clients and their invoices. As a result, streamlined the reconciliation rates and automated payment processing system.
CRIF- KYC ProcessThe Indian Credit Information Company, CRIF has developed two solution software named IDENCHECK and SHERLOCK to improve its existing KYC processes. The framework included digital capabilities in the form of Artificial Intelligence and Machine Learning. As a result, helps to detect fraud and serve with anti-fraud solutions by timely investigation.
Piraeus Bank Group – Credit ScoringA large tech firm SAS offered predictive analytics for Credit Scoring to Piraeus Banking Group. With the use of the software, Piraeus Group speeded its data analysis procedures by 30%. Moreover, it helped them to clearly move between predictions to the future and reestablish effective strategies.

Scope of Predictive Banking Analytics

The significance of predictive analytics in the banking sector is accelerating by leaps and bounds making it extremely necessary to stand upskilled with technological advancement. According to the study of Allied Market Research, the global market size of Predictive Banking Analytics in 2018 was $1.20 billion. Moreover, the valuable figure is projected to reach $5.43 billion by 2026. It further stated that CAGR registrations are expected to experience a boost of 20.80% whereas the Fraud detection and prevention segment is predicted to leverage improvement at the rate of 24.90% by 2026. Thus, the analytics-based service solutions are believed to garner progressive impact on the cash flow trends, customer relationships, client segmentation aspects of the banking.

In the banking sector, predictive analytics is studied considering four major componential segmentation – deployment model, organization size, application, and region. The effective control and performance of the segments can be ensured with the technology experts. Thus, the key players in the predictive banking analytics market are IBM Corporation, Microsoft, Oracle, SAP SE, SAS Institute, Tableau Software, TIBCO Software, Alteryx, Teradata, and other prominent names. These industrial kings have already adopted various strategies such as geographical expansion; collaborations, mergers & acquisitions, and product portfolio expansion to kick start the implementation of predictive analytics in the banking sector.

Final Words

Inarguably, banking is the most essential sector of the economy, and its effective performance amidst the growing digital challenges is the need of an eleventh hour. Thus, the adoption of Predictive Analytics to determine the possible upcoming threats and trends is the first step. Several banks have already paved their way towards the progressive journey, so what’s required now? The skilled human resource and profound infrastructure to support the growth process.

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