Knowledgebase
As the value of the day to day decisions grows, the importance of
Decision Management System deployment becomes undoubted. Organizations adopting Decision Management improve business results by supporting, automating and improving operational decisions. Understanding this a lot of vendors are coming to market with their brand-new solutions. How not to get lost in the variety of options and choose the right solution for you?
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We all get used to looking at
Business Intelligence as a tool for strategic decisions mostly for top management. In looking at today’s business intelligence software market you will find a wide array of various types of dashboards, reporting tools, and KPI monitoring tools to support decisioning at the top levels. We have also seen a few good BI applications for decisioning on the front lines, supporting such vital areas as Sales, Risk Management, Budgeting, and other functions. We can definitely say that a Top-to-Bottom approach is actively developing over the time.
Well, that is natural for many reasons. Top managers make high-value decisions. At the same time, they are both budget holders and budget users which enable them to quickly grasp all possible benefits from business intelligence software usage, and then promote the approach at the departmental level after the success is proven at the middle and upper management levels.
But what’s on the other hand?
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From Basel II to Basel III
The Basel Committee on Banking Supervision has agreed on the new, stricter requirements to the reserve capital rules, based on the decisions made by the Finance Ministers and Central Bank Governors of the G20 countries.
The main aspects of the new Accord, which came into force on the 12th of September, 2010, are focused on increasing minimum capital requirements. The document does not annul; but rather elaborates and enhances the Basel II requirements. Basel III requirements are concentrated around Common Equity since it features the highest level of liquidity and therefore is the most effective instrument for financial loss amortization.
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Recent research and subsequent development in the arena of mass
decision management has made this a highly sought after market in various business institutions (including financial organizations) whose activities are related to retail and even commercial lending. In a hyper competitive environment conditioned by financial globalization and field specifics, the
decision management systems for both global strategy and everyday decision making gain a greater significance. Efficient decision management information systems should not only be able to provide an adequate decision based on the input, but also be comprehensible, easily monitored and managed, and quickly adjustable to account for ever changing market conditions.
One of the main steps to meet these specifics is an effective combination of business process management and business flow management. This requires a solution which can bring a level of semantic technologies according to the potential of AI’s knowledge representations with precise semantics. It also aims to meet the requirements of transparency and compliance, making sure that all roles in the process comply with regional and global rules and regulations. This, as well as the simplicity of the systems maintenance, is provided by choosing and developing a visual design of credit strategies instead of commonly used script-based design of business rules and information flows.
The information system for the decision management in retail loan organizations considered in this article also includes an optimal experience-based combination of such modules as scoring and business rules design. Another essential feature of the system is the ability to support its self-adaptation to the changing environment, stipulated by a great variety of reports, scorecard validation and calibration, flexible rule adjustments and data mining tools.
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It often happens that scorecard accuracy leaves much to be desired. Moreover, the discrepancy between the expected and actual performance of the scorecard is often noticed at the final stage of it’s implementation, when most of the resources have already been invested.
How can we ensure that everything possible has been done to develop a scorecard of the highest quality, and how can we pinpoint and prevent potential errors?
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It can be very dangerous to base lending decisions solely on the behaviors and characteristics of accepted borrowers, or clients. In fact, poor lending rules can be exacerbated and millions of dollars lost if lending institutions do not properly and accurately develop their lending policies, and “acceptance” guidelines. Developing a solid and sound model (or scorecard) using a reject inference can substantially increase the size, and quality of a customer base or portfolio.
In this article, we will look at the use and development of reject inferences for the purpose of raising profits and increasing market share.
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