Essay On Analytics

During the first two weeks, Learning Team “A” studied several objectives. During Week One, we learned how to prepare journal entries to account for transactions related to accounts receivable and bad debt using both percentage of sales and the percentage of receivables methods, ways to distinguish between tangible and intangible assets, the means to identify the entries associated with acquisition, disposal, and sales of plant assets, and closed out the week by distinguishing between revenue and capital expenditures, and the entries associated with each. As we advanced into Week Two, we studied how to differentiate among accounts payable, notes payable and accrued expenses, methods to properly

A community bank is exposed to different types of risks. Hazard or pure risks may or may not result in loss and are, generally, insured, whereas, financial risks are external threats with the potential to affect the bank’s objectives. For the CRO, managing various types of risks is essential for the overall profitability of the bank. To minimize the effect of hazard and financial risks, the CRO will implement ERM or traditional risk management processes to create a program for risk management.

In the past, leaders often relied on their intuition and pursued a hypothesis driven approach to strategic decision making. Field of data science has entirely shifted this paradigm. The advent of machine learning and pattern recognition techniques, in conjunction with the growth of cloud storage and parallelized computational capabilities has given business leaders enormous flexibility to boil the ocean and make decisions entirely based on data.

Business has involved me studying different organisations and analysing their economic, social and financial performance through the use of ratio analysis and interpretation of balance sheets and cash flow forecasts allowing me to draw conclusions. This assisted me to make comparisons amongst businesses and be able to judge their position within the market. Having used Microsoft Excel independently to construct cash flow forecasts and balance sheets indicates I have the ability to interpret a business’s financial reports using basic knowledge to make an informed

Forecasting analytics will enable SYSCO to make appropriate upfront decisions and monitor customers as well as the industry. Extraction and data mining are also useful tools that will positively affect SYSCO’s decision-making process. Lastly, consulting support and employees’ training will facilitate the implementation of the new software in the company. For all these reasons, the use of BI at SYSCO can create a competitive advantage of the company in the industry. However, this competitive advantage depends on the competition – do the competitors use a similar software or by chance the same and do they already have a strong position in the market? Outperforming for example “U.S Food Service”, SYSCO’s main competitor, might be arduous if that company relies on a similar software and already has an eminent role in the

The latest technological development in data analytics and implementation helps organizations gain insights and how to deal with informed decisions.

My current assignment in Technical Analysis, Accounting & Finance (TAAF), has given me the opportunity to development of financial portions of the annual report and comprehensive statement and to preparation of regular briefings for management. My current position prepares analysis and forecast pertaining to one of the fundamental tasks of TAAF, the Workers Compensation Program actuarial model forecast.

Model risk: Banks’ increasing dependence on business modeling requires that our risk managers understand and manage model risk better because the consequences of errors in the model can be extreme.

During my time with Prudential have served many roles within risk management. Originally a Risk Manager in the Operations & Systems department, over the years I have led many initiatives for the Operational Risk area to provide solutions to ensure greater productivity of our managers, the ability to report and analyze the risks and issues within our firm and resolve critical control gaps in the business lines we supported. Most recently, I served as the architect for the ORM department

State Street currently holds a large portion of the global financial assets for it customers. Due to this fact, we are able to see a large sample of the financial transactions that happen at the institutional levels on a daily basis. At the moment, there is much variation between business lines in there analytical position and ability to compete analytically. Over the years, in response to our customer’s demands, we have built a wide range of standard and ad-hoc reports that allow a rear view mirror approach to analysis. Clients can view which of their securities were lent out in our securities lending program, or they can view at what rate there overnight cash balances earned interest. These reports are basic in information, and mostly

With the increased and widespread use of technologies, interest in data mining has increased rapidly. Companies are now utilized data mining techniques to exam their database looking for trends, relationships, and outcomes to enhance their overall operations and discover new patterns that may allow them to better serve their customers. Data mining provides numerous benefits to businesses, government, society as well as individual persons. However, like many technologies, there are negative things that caused by data mining such as invasion of privacy right. This paper tries to explore the advantages as well as the disadvantages of data mining. In addition, the ethical and global issues regarding the use of data mining

In the recent years it has been a trend to use predictive analytics in various fields. The reason to use predictive analytics is to give a lucid view of the

When analyzing Allianz’s risk and return tradeoff, it is crucial to evaluate the risk assessment and related capital losses, prior to considering any financial decision that the company faces. Moreover, it is essential to align the risk appetite and tolerance limits with the riskiness of any new project to determine its worthiness to be undertaken and estimate if the probability of gains is higher than that associated with the loss of capital. With an effective ERM system in

Big data and Analytics have become very prominent areas of study in recent years. Company revenues exceeding more than one million are found to use some or the other form of business analytics. The techniques, technologies, systems, practices, methodologies and applications help analyze the data in the organizations to make critical decisions.

Big data refers to a large volume, velocity and variety of data generated from different types of sources. This data is streamed from sensors and smart devices in real time. It also includes traditional data pertaining to citizens. A smart city utilizes IoT sensors, actuators and technology to connect components across the city. Data gathered from various segments is analyzed and patterns are derived from the collected data which helps in decision making.