# Credit Corp's Consumer Lending Segment¶

Note: All numbers are in the form of $'000 unless otherwise stated Let's import some libraries first... In [1]: import pandas from pandas.plotting import scatter_matrix from sklearn import datasets from sklearn import model_selection from sklearn import linear_model # models from sklearn.ensemble import RandomForestRegressor from sklearn.neighbors import KNeighborsRegressor from sklearn.preprocessing import StandardScaler, PolynomialFeatures from sklearn.linear_model import LinearRegression, Ridge from sklearn.tree import DecisionTreeRegressor from sklearn.metrics import mean_squared_error, r2_score import matplotlib.pyplot as plt  Load the past few years of relevant data. In [2]: dataset = pandas.read_csv("data/ccp-consumer-lending-full-year.csv") print (dataset)   period revenue npbt gross_book_average net_lending 0 FY14 19104 -3522 41465.5 49130 1 FY15 35826 1401 81343.0 51063 2 FY16 52418 8709 117278.5 55077 3 FY17 66374 17596 147714.5 46184 4 FY18 79336 23028 171786.0 52405  Let us create a linear regression model with the whole dataset. In [3]: array = dataset.values X = array[:,3:5] # data = avg_gross_loan_book, net_lending Y = array[:,2] # result = NPAT model = LinearRegression() model.fit(X, Y) # train model # the model's linear regression coefficients print("Coefficients: \t%s" % model.coef_) print("Intercept: \t%s" % model.intercept_) print("\nThe equation would look like...") print("p = %sr + %sl + %s" % (model.coef_[0], model.coef_[1], model.intercept_))  Coefficients: [ 0.2111212 -0.28462548] Intercept: 265.191305252 The equation would look like... p = 0.211121197019r + -0.284625478565l + 265.191305252  Where p = Net profit before tax (npbt) b = Average gross loan book (gross_book_average) l = Net lending for the period (net_lending) # FY19 Predictions¶ ## Based on management's forecasts¶ Assumptions: • Gross loan book to end the year at$199.896m (long story on how I got to something so specific)

• Average gross loan book will be $191.496m • Net lending will be$50m, on the upper range of the forecast. Quoting a high number here will actually reduce NPBT.

In [4]:
gross_book_average = 191496
net_lending = 50000

npbt = model.predict([[gross_book_average, net_lending]])[0]

print("EBIT = $%sm" % (npbt/1000)) print("NPAT =$%sm" % (npbt/1000 * 0.7))

EBIT = $26.4627821213m NPAT =$18.5239474849m


This sits inside the $17 - 19m range forecast by management, so our model is not crazy bad! ## Based on a zero-growth scenario¶ The higher the Net Lending completed by the company, the lower the reported Net Profit due to the way the company provisions the expected lossed upfront. So you get a situation where the NPAT is under-reported, unless the company stops growing its loan book. So what happens with NPAT when the loan book stops growing? • Assume 17.34% of gross loan book is the required net lending to maintain the loan book. • Last 5 years (FY14 - FY18) this figure has been: 14.22%, 17.88%, 16.82%, 13.99%, 17.34%. In [5]: net_lending = gross_book_average * 0.1734 print("\nNet Lending Assumption = %s\n" % net_lending) npbt_zero_growth = model.predict([[gross_book_average, net_lending]])[0] print("EBIT:$%sm" % (npbt_zero_growth / 1000))
print("NPAT: $%sm" % (npbt_zero_growth * 0.7 / 1000)) print("\nNPAT buffer:$%sm" % ((npbt_zero_growth - npbt) / 1000))

Net Lending Assumption = 33205.4064

EBIT: $31.242951362m NPAT:$21.8700659534m

NPAT buffer: \$4.7801692407m