Digital expertise will become increasingly critical in the insurance sector as digitization and machine learning leads to more highly'automatable' insurance according to management consultants McKinsey & Company. Meanwhile a separate piece of research by Accenture found that insurance companies are accelerating the shift to a radically different distribution model, where they say digital will play an increasingly important role in most interactions, and were agents' efforts are being refocused to add more value. And analysis by research outfit Ovum suggests strong investment in digital channels also. According to Ovum, " When it comes to investment, digital channels remains the top area for insurers. However, the significant majority of insurers will be increasing budgets across a broad range of functional areas with no single activity completely dominating spend. This reflects the complex set of priorities that IT groups are being asked to meet by the wider business, simultaneously addressing revenue growth, operational efficiency and regulatory compliance."

During Chevron Corporation's (NYSE:CVX) Security Analyst meeting on March 8, several big pieces of news came out. A day before the meeting, Chevron issued a press release stating that its 54 billion Gorgon LNG facility in Australia had just started producing LNG (liquefied natural gas) and condensate. After originally estimated to be operational by the end of 2014 for under 30 billion USD, the project was delayed as costs skyrocketed. As the operator with a 47.3% stake, Chevron lost a lot of credibility due to the massive cost of its mishaps, as did its partners ExxonMobil (NYSE:XOM) and Royal Dutch Shell (NYSE:RDS.A) (NYSE:RDS.B), who each own 25% of the venture. The first cargo of LNG is expected to be shipped out very soon, potentially marking the beginning of a strong source of growth after all the headaches it took to get here.

When making a decision a trust officer in a bank is confronted with a large assortment of information. In keeping with the postulates of this theory, the main postulates for the analysis of the investment decision process are that there exist: 1. A memory that contains lists of industries each of which has a list of companies associated with it. The memory also contains information associated with the general economy, industries, and individual companies. The set of rules constitutes the structure of the decision processes for an individual investor. It might be compared to the "rules of thumb" of the traditional "expert," but there is an important difference In common with other problem-solving programs, the processes are used iteratively and recursively. Lists of industries and companies are searched for particular attributes; sublists are created, searched and divided again. For example, to obtain a high growth portfolio, the list of companies stored in memory is searched to obtain securities with the desired pand) characteristics.

He then solves the simpler problem and uses the information obtained in the solution of the simpler problem in the solution of the original complex problem.

The editors have provided him with special introductory sections as a ing guide to a thorough understand of the reports.

We study an economic decision problem where the actors are two firms and the Antitrust Authority whose main task is to monitor and prevent firms' potential anti-competitive behaviour and its effect on the market. The Antitrust Authority's decision process is modelled using a Bayesian network where both the relational structure and the parameters of the model are estimated from a data set provided by the Authority itself. A number of economic variables that influence this decision process are also included in the model. We analyse how monitoring by the Antitrust Authority affects firms' strategies about cooperation. Firms' strategies are modelled as a repeated prisoner's dilemma using object-oriented Bayesian networks. We show how the integration of firms' decision process and external market information can be modelled in this way. Various decision scenarios and strategies are illustrated.

In this paper, we propose a new methodology based on the Negative Selection Algorithm that belongs to the field of Computational Intelligence, specifically, Artificial Immune Systems to identify takeover targets. Although considerable research based on customary statistical techniques and some contemporary Computational Intelligence techniques have been devoted to identify takeover targets, most of the existing studies are based upon multiple previous mergers and acquisitions. Contrary to previous research, the novelty of this proposal lies in its ability to suggest takeover targets for novice firms that are at the beginning of their merger and acquisition spree. We first discuss the theoretical perspective and then provide a case study with details for practical implementation, both capitalizing from unique generalization capabilities of artificial immune systems algorithms.

Predicting trust among the agents is of great importance to various open distributed settings (e.g., e-market, peer-to-peer networks, etc.) in that dishonest agents can easily join the system and achieve their goals by circumventing agreed rules, or gaining unfair advantages, etc. Most existing trust mechanisms derive trust by statistically investigating the target agent's historical information. However, even if rich historical information is available, it is challenging to model an agent's behavior since an intelligent agent may strategically change its behavior to maximize its profits. We therefore propose a trust prediction approach to capture dynamic behavior of the target agent. Specifically, we first identify features which are capable of describing/representing context of a transaction. Then we use these features to measure similarity between context of the potential transaction and that of previous transactions to estimate trustworthiness of the potential transaction based on previous similar transactions' outcomes. Evaluation using real auction data and synthetic data demonstrates efficacy of our approach in comparison with an existing representative trust mechanism.

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The financial statements are presented B. Inventory: As described in Note 1A to the on the accrual basis of accounting. Magazine, proceedings, conference financial statements, the Association Effective for 1996 the Association books and publications in process changed its method of accounting for has changed its method of accounting inventory is valued at the lower of investments from "original cost" to for investments from "original cost" cost or market value as of December "fair market value" as required by the to "fair market value" as required by 31, 1996 provisions of Financial Accounting the provisions of newly enacted Statement Standard No. 124.