Predictive Analytics for
Banking & Financial Services
Obtain actionable insights from your data to achieve growth and avoid reputational risk
Rising customer expectations of the flexibility and personalisation being delivered by your competitors and other sectors.
Predictive Analytics for Banking & Financial Services
Banking and Financial Services face several challenges. These are:
- Rising customer expectations of flexibility and personalisation
- No let-up in the continued attention by the regulators
- Rapid shifts in customer behaviour in line with changing technology
- Increasing levels of fraud coupled with significant shifts in its form
- New competitors in the sector
Version 1 can enable you to obtain actionable insights from your data, improve the balance sheet, achieve growth and avoid reputational risk.
Primary Analytics Solutions for Banking & Finance
Design the analytical solution that is best for your business.
Marketing & Customer Experience
Embrace the power of Advanced Predictive Analytics to provide differentiated and personalised customer experience. Use a holistic analytical marketing approach and a comprehensive CRM strategy that will support decision-making, optimisation and automation across different marketing activities and CRM operations in financial institutions.
- Use Enterprise Data to leverage customer intelligence and personalize customers’ banking experience and satisfaction.
- Reveal customer insights to identify new marketing opportunities and effectively address customer needs in real time.
- Develop financial products or services tailored to banking behaviours.
- Optimise offers by determining Next Best Action for individual customers, and drive profitability by presenting the right offers, in the right channel, at the right time.
- Understand the factors behind customer acquisition, loyalty and retention and reduce churn.
Customer Insights
Consolidate all available information from different sources of data, inside or outside the organisation, into a single structured and detailed set of marketing customer attributes and key performance indicators. This includes Financial Positioning, Product Ownership, Transactional Behaviour, Channel Preferences/Usage, Profitability and Customer Value, Credit Risk Information, and Investment and Loyalty Profile.
- Serves as the data infrastructure for any CRM and Marketing Activity.
- Provides the base for all reporting and predictive modelling needs across CRM and Marketing Departments.
- Minimizes overall dependency on IT resources.
- Delivers industrialised customer insights through the deployment of key performance indicators into Banks’ Operational Channels such as Branches or Call Centres.
- Provides improved personalised customer experience and satisfaction, through data visualisation of customer insights into customers’ contact points such as mobile banking or online banking.
Customer Base Segmentation
Develop segmentation schemes that divide customers into useful and actionable segments and reveal customer insights by exploring different aspects of customers’ banking behaviour (spending patterns, demographics, channels preferences, transactions activity, customer value index) during the customer lifecycle.
Design marketing Analytical Strategies and match profitable products, based on customer segments to increase sales and generate profits by focusing on individuals’ customer banking needs.
- Provides a greater understanding of customer profiles, needs and market trends
- Uncovers variation in current, future and lifetime behaviour of customers and forms long-term CRM vision and marketing strategies
- Guides New Products and Services Development tailored to customers’ banking behaviour
- Supports the identification of marketing opportunities and design of targeted offers based on customer value, risk and price elasticity
- Improves the efficiency and planning of banks’ sales channels and networks
Optimise Offers and Marketing Efforts
Maximize profitability by using Advanced Predictive Analytics and propensity models for Banking to identify best prospects for new product offerings, increase existing products/services usage or substitute existing products and services with new more profitable ones.
Predict the response to an offer and minimize marketing costs by determining customers’ Next Best Action or offer, weighted on banks’ profitability metrics and individual customers’ risk scores and assessments.
Customer Retention, Acquisition and Loyalty
Adopt a proactive analytical approach to identify the risk factors that influence customer acquisition and retention, and prevent churn effectively, early in time to enhance customer loyalty.
Determine early warning signals such as a reduction in transaction volume, credit spending or in deposit balances and send customized offers to the people most at-risk of churn.
Combine Customer Insights from Segmentation models and offers optimisation to develop targeted retention campaigns.
- Reveals changes or patterns in behaviours, indicating factors which may cause disloyalty or churn
- Predicts churn and analyse risk indicators early in time with confidence
- Develops effective and personalised customer retention strategies and improve products or services design to enhance customers’ loyalty
- Examines Customer Service and Satisfaction, by using Text Analytics and analyse customer interactions such as Branch visits or call centre calls
- Identifies the reasons behind customers’ disloyalty inside the organisation
- Helps to Understand previous successes and challenges in trying to attract more and better customers
Banking & Financial Services Case Studies
Case Study – OTP Bank
OTP Bank wanted to improve the speed and efficiency of its mortgage, loans and lease application processes, and make accurate, evidence-based decisions more rapidly than the competition. Upon implementing IBM SPSS Modeler, they can now uncover patterns and predicts risks associated with each applicant. As a result, the bank can quickly identify and approve those with low risks, accurately assessing borrowers’ credits to develop more precise revenue forecasts.
Case Study – Fiserv
Small and midsize banks and credit unions seek to attract, retain and grow profitable customer relationships while competing with the analytic capabilities of new mega banks. Working with IBM and using SPSS software, Fiserv is turning billions of transactions into actionable insights that help these banks better target offers and maximize their marketing dollars.
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Discover More Industry-Specific Solutions
Version 1’s SPSS experts can consult and deliver a wide variety of analytics solutions across a broad range of industry sectors. Find out more at the links below.
