And from the date of partial payment onward, charges will be on remaining Rs. Any SBS installment will be treated as a retail transaction and services charges will be levied.Įxample: If a cardholder shops for Rs.10,000 in a month and pays Rs.5,000 by the due date, the Bank will first charge him/her on Rs.10,000 till the date of partial payment i.e Rs.5,000. For a second time revolver, service charges will be levied on the balance carried forward from the billing cycle date and on all transactions from transactions dates appearing in the statement. For a first time revolver, service charges will be levied on all transactions appearing in the current statement and previous month’s statement from the date of the transactions. #Alfa vision pre cash trans fullRevolver: Revolver is a cardholder who make minimum (5% of outstanding) or less than full payment. Transactor is the cardholder who pays off all his/her liabilities (mentioned in monthly statement) within the due date and no services charges is levied on such an account.Įxample: If a cardholder shops for Rs.10,000 in a month and pays the entire amount of Rs.10,000 within the due date, being the Transactor no service charges will be imposed, if there is no BTF and cash transactions / Call & Pay. Transactor: Transactions other than cash advance / BTF and fees are called retail transactions. In case of partial payment, please see the details under the heading Revolver. 10,000 on 06-1-2008, he /she will also be charged with a 5 day service charge on Rs. The service charges on both the aforementioned facilities are taken from the date of transaction (the customer is either a Transactor (explained below) or Revolver (explained below)).Įxample: If a customer avails cash advance or BTF of Rs.10,000 on 01-1-2008 and pays the entire Rs. Bank Alfalah also offers an easy and convenient way to settle their payments on all their existing credit cards with our Balance Transfer Facility (BTF). The following are different scenarios to levy service charges on the monthly bill statement.Ĭash Advance / BTF: Bank Alfalah cardholders can avail our Cash Advance Facility up to 50% of their credit limit. Alfa Zindagi – Alfa Zindagi by EFU Life.Alfalah Orbit Rewards Terms and Conditions.Alfalah Insurance Car a Vaan Motor Plan.Jubilee Rahat Savings and Protection Plan.High classification accuracy suggests that automation of crash typing and consistency checks can be accomplished effectively by using extracted latent themes from the crash narratives. The classification accuracy in the held-out sample ranged between 89.3 % for sideswipe crashes to 99.2 % for single motor vehicle crashes. The efficacy of generated topics in classifying crashes by type was tested using a machine learning algorithm, Random Forest. The centrality and association between topics varied across crash types. Eigenvector centrality measure in network topology showed that event-related topics were consistently central in articulating the crash occurrence. Topics with the highest prevalence for the angle, head-on, rear-end, sideswipe and single motor vehicle crashes were crash at stop-sign, crossing the centerline, unable to stop, lane change maneuver and run-off-road crash, respectively. The main advantage of the STM over the other topic modeling approaches is that it allows the researchers to discover themes from documents and estimate how the topic relates to the document metadata. The structural topic modeling (STM) and network topology analysis were used to generate and examine the prevalence and interaction of themes from the crash narratives that were mainly categorized into pre-crash events, crash locations and involved parties in the traffic crashes. Ten years (2009–2018) of Michigan traffic fatal crash narratives were used as a case study. The present study aims at utilizing crash narratives complemented by crash metadata to discern the prevalence and co-occurrence of themes that contribute to crash incidents. The proliferation of digital textual archives in the transportation safety domain makes it imperative for the inventions of efficient ways of extracting information from the textual data sources.
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