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