7 Ways to Leverage Data Science in the Banking Industry

Data-Driven Sales Initiatives

Personalized, customer-centric sales and marketing efforts yield better ROI than a one-size-fits all approach. However, studies have shown that 94% banking firms fail to deliver on their promise of personalization.[2]  With data and analytics-driven initiatives, banks can gain access to extensively segmented customer demographics, and target marketing initiatives by mapping products, offers, cross-selling initiatives and promos, to the most suited customer segment. Detailed analyses of past sales pitches can ascertain sales dos/don’ts, and improve ROI generation for future initiatives.

Becoming Tech-Forward

From AI-powered virtual banks to fast-response chatbots, next-gen technology is heralding in superior customer service. Banking institutions must invest in becoming tech-forward so that they align to the impact of real-time socio-economic market changes on customer needs. Furthermore, they must also leverage predictive analytics and deep dive into market trends, customer behavior, social media buzzwords, etc., to predict future customer needs and reach the customer before their competitors do.


Augmented Fraud Detection

Rise in e-banking & e-commerce has also led to a steady rise in online fraud. Unusual patterns in transactions can be detected through Machine Learning and further analyzed to identify ill-intentioned activities. Additionally, extensive analysis of historical transactional data can help define most common characteristics in fraudulent transactions – such payment channel most prone to fraud, time of fraud, etc., The model accuracy will further improve overtime with feedback, and detect patterns leading to fraud.

Improved Risk Management

Credit Risk Assessment is a fundamental function for banks as it helps determine the success rates of loan repayment. With analytics, the performance of the applicants throughout the fiscal period that they coalesce with the bank for, can be scrutinized. Different customer traits can then be mapped out an segmented to classify and rank loan applicants who are most likely to default, in order of priority.

Robust Customer Relations

By assessing historical transactions and consumer behavior, banks can identify customers prone to attrition, determine customer lifetime value, prioritize at risk customers and take remedial measures. Machine learning and AI can also help create customer service chatbots that perform routine tasks, catalyze customer grievance redressal, and collate data to preempt customer issues and provide improved services.

Improved Customer-centricity Capabilities

With advance customer classification, segmentation and clustering customer traits can be defined keeping in mind the schemes that customers subscribe to, their transactional trends, etc. Based on customer preferences, their services can be personalized. Products and services too can be custom-marketed. Robust real-time analytics capabilities further ensure that the banks can swiftly adapt to latest trends in the market and tweak customer offerings in lieu of the same.

Customer Payment Analytics

Payments are among the most pivotal banking functions. To ensure secure transactional activities and safeguard customer privacy, companies need to analyze internal and external data sources, networks, overall payment traffic. The analysis will also help improve overall quality of payment data and help determine the channels through which future products and services are likely to be purchased. With mobile applications and customer consumer profiles, banks also offer customized payment promotions by scrutinizing historical transactions.

Associate Principal, TheMathCompany

Shantanu Seth