I need proof reading please

could you please go through the following text and mention the points in which you see a problem:

Although providing a cost-benefit analysis for posteriori prevention is easier, it does not necessarily make sense to conduct a cost-benefit analysis on the other two types of prevention, i.e., the preceding and the active preventions. Active prevention, for instance, cannot be easily evaluated by cost-benefit analysis because outcomes cannot be determined accurately. To understand the difficulty following example can be considered.

In 2018, American Public Benefit Agency has paid 100 clients through Insurance Program. Among these clients, 10 unentitled benefit recipients have been recognized. They have received $1000 in total. In 2019, APBA implements a new preventing strategy to prevent illegitimate applications upfront. At the end of the year, Insurance program reports the results: 110 clients have been served. 7 fraud have been discovered with total of $800 overpayment. In this situation, one can conclude that preventive tools have been effective because the department has lost $300 less than last year. However, nobody can certainly claim that the saving is merely result of prevention strategy. Perhaps there are other unknown elements that have affected the result and we do not know them. The case will be much more complicated, if we open our eyes to the reality that in social benefit programs several controlling tools are working simultaneously. In such case determining the effectiveness of each one individually is more or less impossible.

In such cases that the monetary value of prevention is not transparent, the preventing tools can be analyzed differently. As it was discussed above, cost-effective analysis might be helpful here. The analysis gives the preventive tools a rate determining whether they have been effective. In cost-effectiveness analysis a range of metrics should be accounted:

1- Number of fraudulent activities prevented: The first important metric to examine effectiveness is the number of fraud attacks prevented. The question is how many fraud attacks have been prevented with the current fraud preventions tools. The number is significant when it is displayed as a portion of total prevented application for any reason. In this way, public services can measure how prevalent fraud is in their business and how good their current strategy is in preventing fraudulent activities. They should also confirm that these prevented applications are fraudulent, and not include prevented applications that turned out not to be fraudulent, as this can give an unclear picture.

2- Money Lost to Fraud: It is also important to monitor how much fraud is costing the benefit programs. This can be measured as how much money is lost to fraud as a total or compared to the total correct payments. A successful fraud prevention strategy will reduce the amount of money lost to fraud.

3- False Positive: it is the number of clients who have been wrongly denied. False positive in the context of social programs can lead to erode the reputation of the programs. One way to measure false positives is to look at how many applications are approved. This metric can give insight into the number of false positives. Ideally, social programs want all non-fraudulent applications to be approved. If the number of approvals decrease, it either means more fraud is being prevented or normal applications are not being approved. If the number of fraudulent applications is not increasing, it is likely an issue of false positives.

4- The Bottom Line: The bottom line for analyzing a fraud prevention tool is to make sure the benefit programs are getting a high return on prevention investment. They should not be spending more on fraud prevention than what they are gaining by implementing the prevention tools. If the return is low, it may be that it does not work to prevent fraud, or that the programs are losing revenue due to high false positives.