Question

What are some positive impacts of the implementation of artificial intelligence or machine learning that you could use with fund accounting to counteract the issues of agency readiness, budgetary constraints, or security?

Answer 1

Machine Learning or Artificial intelligence :

• Machine learning is the leading edge of artificial intelligence.
• It is the subset of artificial intelligence where machines use algorithms to interpret data from the world to predict the outcomes. To learn from sucess and failures.
• As machines infiltrate accounting tasks to take over more mundane and repititve tasks, it will free up more accountants and book keepers to spend more time using their professional knowledge and interpret data which will be provided to their clients.

Positive impacts of implementation of artificial intelligence or machine learning to counteract issues :

a) Casual agency :

• When artifacts are said to be in the agents sense the emphasis on the agents efficacy. This type of agency fills the gap since too little attention has been given to the ways in which the technology shapes what happens in the world.
• Treating artifacts as the agents properly frames them as th significant constituents of the human world.

b) Intentional agency :

• Intentional agency involves the intentional actions that are used by the intentional agents which are the entities that act intentionally.
• Casual agency and intentional agency share the element of casual efficacy.But in intentional agency agent's intention begin the chain of casuality.
• Intentional actions of top management come into play for resloving agency issues.

c) Underlying data are often the source of bias:

• Underlying data rather than algorithm itself are the most important and main source of issue.
• Models are trained on the data containing human decisions on the data that reflect second order effects of societial or historical inequities.
• For example, word embeddings ( a set of natural language processing techniques ) may exhibit the gender stereotypes found in the society.

​​​​​​​d) Budget constraints in the training phase countered :

• Classification problems for which feature labels are unknown in the trianing phase which are commonly countered in crowdsourcing.
• The algorithm can acquire the label of a desired feature and each label has a fixed cost.​​​​​​​

e) Budget constraints at testing phase countered:

• Budget constraints at testing phase are countered using feature labels for the test.
• crowdsourcing prediction markets,medical diagnosis,sensor data aggregation are the new prdictions used in the testing phase to acquire the data required for the specific instance and are used to reslove the issues.

​​​​​​​f) Risk Assessment :

• Machine learning could facilitate risk assessment mapping and pulling the data from every project of the company had ever completed to compare it with propsed project.
• Assessment of risk helps in counteracting the issues of security through fund accounting.

​​​​​​​g) Artificial intelligence for financial security :

• Artificial intelligence provides an adaptive technology that can analyze the activity of attackers.
• Artificial intelligence with the potential to provide customized security systems have different security requirements.
• Artificial intelligence is fully capable of unertaking data analysisin an automated fashion to identify and flag anomalies.
• Through amchine learning algorithms artificial intelligence can predict potential attacks on the current data.