Supervised Learning on Sequential data I am doing credit risk modelling on costumer transaction data a part of which looks like this : 

str(x)
'data.frame':   412516 obs. of  26 variables:
 $ Tenure           : num  1.26 1.25 1.26 1.31 1.32 ...
 $ Product          : Factor w/ 24 levels "BACKHOE LOADER",..: 4 4 4 9 9 9 9 9 9 9 ...
 $ Net.Exposure     : num  333339 528049 327335 350000 460000 ...
 $ OD.On.31.01.2017 : num  0 90386 0 0 1099692 ...
 $ LM.Bucket        : Ord.factor w/ 11 levels "0"&lt;"1 TO  30"&lt;..: 1 1 1 1 11 11 11 11 11 11 ...
 $ Bucket           : Ord.factor w/ 11 levels "0"&lt;"1 TO  30"&lt;..: 1 3 1 1 11 11 11 11 11 11 ...
 $ Billing          : num  65380 0 8800 6339 8331 ...
 $ Fin.IRR          : num  13.5 14.6 14.6 18.1 23.3 ...
 $ NPA.Flag         : Factor w/ 2 levels "No","Yes": 1 1 1 1 2 2 2 2 2 2 ...
 $ Inst.Due         : num  0 0.85 0 0 3 3 3 3 3 3 ...
 $ FR.On.31.01.2017 : num  65380 0 38940 35043 499860 ...
 $ POS.On.31.01.2017: num  56453 0 32920 33368 293943 ...
 $ Del.String       : int  2 1 1 1 53720 53720 53720 53720 53720 53720 ...
 $ Territory        : Factor w/ 43 levels "AGRA","AHMEDABAD",..: 41 41 41 41 41 41 41 41 41 41 ...


The variables like OD(Overdue) and LM.Bucket( How many months he has been due on his loan payment till last month) change every month .I have 2 tasks :Predict Bucket and NPA Flag(Non performing asset)

I built a model for this based only on the Jan data(x). But my question is since these variables change every month, should i treat this as a sequential data and build a deep learning model(HMM/NN) on it? If i should what should I do with the static variables like Product type etc.?

I asked my boss regarding the same and he said it shouldn't be done because external economy factor change with time. Is that a reason for concern?
This basically asks for a recurrent network, like the LSTM. But if you only have 2 properties that are dynamic, I don't think you will have as much luck because they might be affected from external factors as your boss said. However, this will happen regardless of the model you're using. 

You should not throw away static properties, unless they are the same for every test case. E.g. farmer/politician/baker category should always be included, but you call this 'static' but it's not completely static as it is not the same for every sample in your test cases.