I would be extremely benefited if someone can send me some mock FICO implementation/support projects where I can learn how things are actually done in the corporate world. The mock projects can be of any industry.
BDC is a very good feature but for big projects, this is not recommended. Recording transaction, coding exercise, preparing FS and testing for each country is cumbersome. For small projects with one company code or less data this is a good option.
For Qualitative content, create checklist for each object. Below are checklists I have created for each object. Share this checklist to users to avoid basic mistakes while filling the templates. This is an example and can differ for readers in their projects. Some of the points are repetitive in different objects. Please ignore as this is for user awareness.
Sample uploads for few transactions for each object should be carried out before the final upload. User with the help of business support team should review the sample upload and give the confirmation for correctness. This step could prevent business from big blunder as well as big spoiler.
This is most sensitive area of DM activity. In Global/large projects, data migration for multiple countries are carried out simultaneously. Consultant will have to deal with different kind of users. Approach adopted for one country might not work for another country. There are different regions like EMEA, APAC, Africa, Middle east etc. Every location has their peculiar style of working. Sticking to standard Data migration plan and modifying the approach as per country culture/time zone or user working style is very important. Carrying over recent DM upload experience is good but need to be adapted as per their requirement.
Creating cost/Profit Center hierarchy is very easy for consultants. But for users its tedious task. In some projects, users are handed this responsibility. User will create each node one by one and this will be very time consuming. Work along with them for faster processing. Educate them with tricks of creating multiple nodes at same level. This will save lot of time.
Summary
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As with any IT company , you move from one project to another in due course of time. All the hardwork you did to understand the functionality of AUT is obsolete in the new project. This is typically true if you are switching projects across domains say telecom to healthcare.
In case of SAP, the functional knowledge you acquire is portable and can be used in other projects. Suppose you are switching jobs. In your old company you were testing billing software for Vodafone. What is the likely hood that the same project is available in your new company ? Next to Zero.
testRigor has a record-and-playback functionality built-in, as well as autonomous test generation for new projects. Other aspects worth noting are great documentation and excellent customer support for all paid tiers.
This page provides you with Sap Fico Consultant resume samples to use to create your own resume with our easy-to-use resume builder. Below you'll find our how-to section that will guide you through each section of a Sap Fico Consultant resume.
The first is a portfolio-level dataset that reports, for each bank, granular monthly information on the balances, revenues, and expenses of the credit card portfolio, including interest income, interchange income, fee income by type of fee, interest expenses, noninterest expenses, and provisions for loan losses. The second is an account-level dataset that provides information on how the account is used, including balances, total purchases made during the month, finance charges, and fees accrued.6 We use a constant sample of 13 banks during our sample period. As the Y-14M data include all of the largest credit card issuers, our sample covers about 80 percent of credit card balances reported in the regulatory Reports of Condition and Income (Call Reports).
Note: Return on assets for Y-14 is calculated as the sum of interest income and noninterest income minus interest expense, noninterest expense and loan loss provisions, divided by average credit card balances. Y-14 reflects a constant sample of banks. Return on assets for Call Report is calculated as quarterly income divided by average quarterly assets. Prior to 2010, some credit card banks held large portfolios of credit-card-backed securities off-balance-sheet. Therefore, average quarterly assets prior to 2010 include on-balance sheet credit card securitizations.
Next, we turn to the NTM. Figure 3A plots NTM on the left axis, in red, and the share of purchases on the right axis, in gray. The share of purchases generally grew steadily during the sample period, apart from a sharp increase in 2021 driven by surging credit card purchase volumes and declining revolving balances. 15 At the same time, NTM declined significantly from 2014 to 2019, dropping from 0.6 to less than negative 0.6 in five years.16 Figure 3B decomposes NTM into rewards expense and the remainder of NTM. As the figure shows, the steady decline in NTM is due in large part to an increase in rewards expenses, which rose from a quarterly average of 3.5 percent in 2015 to around 4.4 percent in 2020, an increase of about 25 percent.
Finally, in figure 4, we turn to the last major component of profitability: late and other usage fees (in red). Late and other usage fees include overlimit fees, foreign exchange fees, cash advance fees, and other fees associated with using a credit card. As mentioned before, annual fees are included in NTM.18 On average, late and other fees comprise 16 percent of profitability.19 This makes fees the second largest driver of profitability, after the credit function. The decline in fees starting in 2020:Q2 was due to many lenders implementing fee waivers as part of the pandemic response. The Other component of credit card profitability (in gray), which includes balance transfer income, prepayments, and other miscellaneous factors, increased gradually during the sample period and comprises approximately 7 percent of profitability, on average.20
6. For computational simplicity, we use a 1 percent random sample of the account-level data, which corresponds to about 3 million accounts, and almost 280 million observations, for the January 2014 to December 2021 period. Return to text
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