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maximization reduction financial

Pursuing a maximization reduction financial strategy comes with the obvious risk that the company may be so entrenched in the singular strategy meant to maximize its profits that it loses everything if the market takes a sudden turn. For example, a company may find that it gets the most profit selling games so instead of keeping a balanced inventory, it invests solely in buying to sell. If the it goes out of favor or the makers of the games begin to limit the price that can be charged for the system, the company that relied solely on its investment this could lose everything. Similarly, if a company focuses only on maximizing its profit, it may miss opportunities for investment and expansion.

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maximization reduction financial
Improve Fraud Prevention
Fraud represents a major cost to financial institutions—and one that threatens to escalate with the growing sophistication of
financial criminals. Many banks and credit unions continue to manage fraud according to institutional silos, delegating this
responsibility to individual business units and product types. Institutions should take steps to integrate fraud management
into a centralized, cross-product function that enables the sharing of resources and data, and better coordination of tactical
approaches—resulting in reduced fraud losses and a more consistent customer experience. Institutions should also make use of
the latest detection technologies to reduce their fraud costs: neural networks and predictive software technologies represent
just a couple of innovative solutions being used by institutions to cost-effectively detect and prevent fraud in real-time. Because
fraudsters follow the path of least resistance, institutions that lag the rest of the industry in implementing advanced prevention
and detection solutions make themselves.
Deploy Analytics
Because of the major structural changes that financial institutions are making to their retail product offerings in order to restore
or maintain profitability, customer analytics are more important than ever. Institutions must use analytics to segment customers
in order to better understand revenue opportunities and re-pricing options. They must conduct analyses on revenue sources,
consumer transaction behavior, and sensitivity to fees across their customer bases and within specific customer segments—
and then use the resulting insights to design new pricing strategies and inform revenue replacement efforts. The savvy use of
customer analytics is critical to evaluating customer profitability, predicting consumer behavior, optimizing relationship pricing,
and developing cost-effective marketing tactics.

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