Predictive Analytics for Banking & Financial Services

It is hard to identify anyone in the sector who has not faced challenges during the turbulence since 2008. Whilst for many there is optimism that this is the year of a return to more stable times, for some, the choppy ride continues. Amongst the challenges and opportunities you face are:

  • Rising customer expectations of the flexibility and personalisation being delivered by your competitors and other sectors.
  • No let up in the continued attention by the regulators.
  • Rapid shifts in customer behaviour, such as the 36% decline in branch usage seen by RBS in 5 years.
  • Rising levels of fraud coupled with a major shift in its form.
  • New challengers from both within the sector and outside of it including Apple Pay, ClearScore and others.

Presidion can enable you obtain and action insights from your data, to improve the balance sheet, achieve growth and avoid reputational risk.

Featured Solution

Marketing and Customer Experience


Embrace the power of Advanced Predictive Analytics to provide differentiated and personalized customer experience. Use a holistic analytical marketing approach and a comprehensive CRM strategy that will support decision making, optimization 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 behaviors.
  • Optimize 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.
  • Combine Enterprise Data, Big Data, Social Media, Text Analytics and Social Network Analysis to predict major CRM events and resolve issues using Advanced Predictive Analytics applications for Marketing and Banking.

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Case Study

Addressing Customer Satisfaction Issues in Real Time.

  • Production cycle reduced by two thirds thereby freeing analyst’s time to focus on strategic initiatives
  • Ability to efficiently parse the thousands of verbatim responses collected each cycle
  • Ad-hoc requests from internal customers can now be turned around in hours rather than days

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Case Study

  • Fiserv clients are experiencing anywhere from 50 to 60 or more percent savings on the costs side, on the processing side.
  • For Fiserv, one of the main values of smarter computing is the ability to now start to build out solutions that weren’t possible a few years ago.

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White Paper

In this white paper, you will learn how your financial services organization can use analytics to understand customer profitability and lifetime value across all products and business lines!

You will read about:

  • Step 1: Consolidate customer information
  • Step 2: Predict what customers want
  • Step 3: Personalize customer interactions
  • Step 4: Optimize your predictions

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Use Cases

1. Gain Customer Insights


Consolidate all available information from different sources of data, inside or outside the organization, 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, 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 from IT resources.
  • Delivers industrialized customer insights through the deployment of key performance indicators into Banks’ Operational Channels such as Branches or Call Centres.
  • Provides improved personalized customer experience and satisfaction, though data visualization of customer insights into customers’ contact points such mobile banking or web banking.


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Case Study
First Tennessee Bank - Banking on Knowledge

See how First Tennessee Bank achieved a:

  • 3.1% increase in marketing campaigns
  • 600 % ROI through better campaign efficiency
  • 20 % reduction in mailing costs


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2. Improve your Customer Base Segmentation

segmentation1Develop 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 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 greater understanding of customer profiles, needs and market trends
  • Uncovers variation in current, future and lifetime behaviour of customers and form 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

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Case Study
Union Investment GmbH
Predictive analytics used to fine-tune market segmentation and drive higher sales

  • Identified 10 times more characteriscs per customer that contribute to the decision on whether to purchase.
  • Increased sales of mutual funds through direct sales agents.


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Union Investment
Union Investment achieves precision-targeted marketing – Gaining predictive insight into investor behavior.


  • Greater return on marketing spend
  • Improved customer service
  • Smarter product development


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3. Optimize 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 customer Next Best Action or offer, weighted on banks’ profitability metrics and individual customers’ risks scores and assessments.

Combine data from Social Media for accurate targeting or Combine Social Network Analysis with propensity models to uncover customer’s relationships inside the organization and focus on targeting customers that can influence other customers as well.


  • Helps to understand the profile of customers who are best prospects for offers and provides recommendations on product and services
  • Provides personalized and differentiated Offers based on Customer Profile, profitability index and risk adjustments
  • Optimizes campaigns’ response rates and minimizes marketing costs
  • Supports Sales processes, drive profitability and optimize customer lifecycle value


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Case Study
Getin Nobel Bank

Personalizing offers to meet customers’ specific banking needs raises savings deposits by 20 percent.


  • Raised its savings account funds balance by 20 percent
  • Increased the number of active account-related cards 1.7 times and the number of transactions on those cards 2.5 times.
  • Can use the solution to assess campaign effectiveness and constantly improve multichannel marketing efforts.
  • Received the 2015 Portfele Wprost award for best retail bank in Poland for delivering products that best met individual customers’ needs.

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Leading bank uses Predictive Analytics to lower costs and generate higher returns on marketing campaigns.


  • Acquisition, cross-selling and retention campaigns are more accurately targeted.
  • Rabobank can now determine the best way to approach the customer.
  • Local banks and advisors have access to timely and targeted leads
  • Marketing initiatives can be launched much faster thanks to user- friendly models – completion time decreased by up to four weeks.

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Case Study

See how Predictive Analytics is helping DekaBank to:

  • Better determine the characteristics of typical purchasers
  • Target the customers who are most interested in certain mutual funds
  • Optimize marketing

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ANZ Bank

ANZ Bank leverages IBM Big Data & Analytics to gain a comprehensive view of their customers & their needs

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4. Improve 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 reduce in transactions volume, in 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 optimization to develop targeted retention campaigns.

Combine Social Network Analysis in Churn Prediction to uncover customer’s relationships inside the organization and proactively improve retention rates.


  • 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 personalized 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 organization
  • Helps to Understand previous successes and challenges in trying to attract more and better customers


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Case Study

First Tennessee Bank - Winning New Business Online

See how First Tennessee Bank is winning new business online with streamlined customer journeys across all devices.

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Other Solutions

Complaint & Claim Handling

Regulators continue to review not just historical issues, but also agreements subsequently made in relation to them. Coupled with this, with access to Social Media, the Voice of the Customer has never been a more powerful force:

  • Understand how to prioritise complaints and claims, to optimise resources and deliver outcomes which achieve higher customer satisfaction
  • Prevent reputational damage by focusing resources on those issues and customers, where the risk is greatest.

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Fraud Detection, Investigation and AML


Money launderers and fraudsters continue to work night and day, shifting to channels offering the greatest opportunities:

  • Move beyond rigid, historical, rule based detection approaches to analytics approaches that learn from the data, to identify high risk transactions in real time.
  • Utilise law enforcement and national security agency grade technology to investigate cases

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Case Study

Statistical modeling used to identify hidden predictors in loan applications, reduce defaults and increase capital reserves.


  • Significantly contributed to the increase in the bank’s capital reserve by reducing loan defaults
  • Reduced loan defaults with increased accuracy of predictive models, pinpointing low-risk applicants and high-risk accounts
  • Increased the accuracy of revenue forecasts by predicting the impact of changes in loan payments as well as fluctuations in the finance industry

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Case Study

Bancolombia achieved a 40% improvement in the quality of it’s suspicious transaction reporting.

  • Achieved a 40% improvement in the quality of its suspicious transaction reporting
  • Productivity savings of nearly 80% while increasing reporting by 200%
  • New flexibility in adapting its models to meet rapidly changing money laundering techniques

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Related Resources

Download your info-pack for Banking & Financial Services!

Gain access to the latest Predictive Analytics Case studies, White Papers and Reports in your industry!

  • "Presidion were brilliant. They helped us turn our situation around so that we are now spending 80% driving outsights out of our data and 20% of our time actually collating data. We are now able to concentrate our time and resources implementing our customer strategy."

    Stephen Moran
    Bank of Ireland
  • "We have no intention of changing our software, as we have been highly satisfied with all aspects of the solution. Simply put, our daily operations would be impossible to solve satisfactorily without IBM SPSS software."

    Tamás Tóth
    OTP Bank
  • "Without Modeler, it would be difficult to detect relationships between originators and beneficiaries who send and receive transfers internally. Criminals use these networks precisely because they won’t be detected by traditional systems."

    Felipe Correa
  • "The predictive modeling solution proved to be a good ally in helping us structure new ways to approach anti-money laundering responsibilities within the organization."

    Francisco Ruiz
  • "With IBM SPSS Modeler, we have been able to transfer 80 percent of our money-laundering detection resources into bringing new business into the bank."

    Francisco Ruiz

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Would you like to see how Predictive Analytics can help you achieve your goals?
Request a Consultation with a member of our insight team!