This week's assignment relates to the Natwest case materials. The Natwest case involved major AML $($anti$-$money laundering$)$ compliance breaches at a large U$.$K$.$ bank.

Please create a Bayesian network to model illegal "structuring," as outlined in the PPT deck, To SAR or not to SAR. This PPT deck shows a model that I built with Bayesialab, another BN platform similar to Agena.ai$.$ What we are modeling is the probability that a banking transaction is part of an illegal structuring scheme.

Use this presentation for reference in creating Bayesian Network $2$ in Agena.ai$.$ Your BN $2$ should be very similar to that shown in the BN $2$ Zoom Walk$-$through, but should include at least TWO additional nodes: one control or trigger, and one consequence of structuring. These s$/$b nodes that YOU find through your Natwest readings or by asking chatGPT or another AI tool.

Upload your $.$cmpx file here. You should include in the model a text box that briefly explains the model's purpose and how it works. This model should be applicable to all depository institutions, not just Natwest. So the name "Natwest" should not appear in the model, but the Natwest case facts should inform the structure and causal context.

The model should include THREE scenarios: Prior, Post $1,$ and Post $2.$ "Post" means "posterior," or after entry of evidence. Post $1$ should show one piece of evidence $($of any state$)$ in a control or trigger node. Post $2$ adds to Post $1$ one piece of evidence in a consequence or symptom node. Run the model with all three scenarios visible in the node monitors, like this:

In a text box or boxes in the model, report probabilities and odds of Illegal structuring for each of the three scenarios. Explain very briefly what these figures mean.

The odds of an event $=$ p$($Event $=$ True$)/$p$($Event $=$ False$).$ If p$($True $=30\%),$ then the odds would be:

$30/70=0\mathrm{.}43$