Credit Management Solutions

Document created by Makenna.Brei Advocate on Aug 2, 2017Last modified by on Sep 8, 2017
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FICO® Optimization Solutions for Credit Management enables:

  • More effective, timely decisions that result in improved performance outcomes
  • Greater insight into impacts of future decisions
  • More precise control over desired outcomes that can adapt over time
  • Greater agility in quickly changing and deploying decision strategies
  • More tailored decisions that reflect individual customer preferences
  • Increased transparency into the decision-making process to facilitate governance


How do you strategically manage your credit line to ensure your portfolio grows and remains profitable?

Credit card lending keeps growing and diversifying, with new entrants disrupting every element of the industry. As margins tighten, regulations evolve and customer engagement demands laser-focused precision and execution, basic BI and predictive analytics fall short of helping financial services stay one step ahead. At the end of the day, how do you dynamically manage your credit line to ensure your portfolio grows and remains profitable while also controlling for risk?


FICO has over 15 years of experience in driving profitable Credit Line outcomes in financial services using optimization. Our FICO® Credit Line Optimizer solution helps lenders develop, assess and improve the decisions that drive customer interactions in order to increase profitability, reduce risk and deepen customer relationships. It’s built upon advanced action-effect modeling, with simulation and optimization techniques that allow business analysts to discover better, faster decision strategies that balance trade-offs between cost, risk and reward, while also factoring in economic and market conditions.


Making the leap from predictive into

prescriptive analytics

Action-effect modeling

Exploration with simulation

and scenario analysis

Moving beyond predictions about what will likely happen in the future,

prescriptive analytics help derive a decision strategy that will prescribe

the actions required to achieve the desired outcome. Credit Line Optimizer converts a framework of predictive models into a prescriptive decision strategy. Using action-effect modeling and simulation, business analysts can pinpoint optimal credit line decisions at varying junctures in the account lifecycle.

Credit Line Optimizer uses a range of data-driven predictive modeling

techniques, including a special type of model that predicts a variety of

outcomes based on the decision. Action-effect models provide an advanced method for linking behavioral outcomes to the decision. By accurately assessing the impact of different decisions on a customer’s reaction, banks can better identify and overcome any performance bias in the data, and accurately assess how changes in decisions will impact outcomes

The true power of optimization to solve business problems comes from

running comparative scenarios that show where you are today against

an “efficient frontier” of potential opportunities. Credit Line Optimizer provides a wide array of simulation options that allow users to modify constraints and explore trade-offs in order to identify the optimal strategy that best meets defined goals.


CASE STUDY: North American Bank balancing profit and risk with credit line increases

Other Credit Management Examples  |  Case Study

Client: Large North American Bank

Challenge: Optimize the credit line management platform in order to drive more profit organically and limit the need for management intervention

Results: Increased credit line utilization and reduced losses via optimization:

  • Response Rate +2%
  • Balances +9%
  • Loss Rate -9%
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Managing credit lines for several million customers, the bank wanted to find a better balance between increasing profits and minimizing risk. Partnering with FICO, the bank developed a suite of action-effect models, leveraging performance data from its current strategy. By fitting these models into an optimization framework, the bank was able to simulate various optimal scenarios in order to explore new segments and ultimately deploy a stronger strategy that both increased credit line utilization and reduced losses. Significant results led to applying optimization throughout other parts of the portfolio.

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