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Can I Grade Loans Better Than LendingClub?

by Ty MickOctober 28th, 2020

Too Long; Didn't Read

In case you missed it, I built a neural network to predict loan risk using a public dataset from LendingClub. Then I built a public API to serve the model’s predictions. That’s nice and all, but… how good is my model?

Companies Mentioned

featured image - Can I Grade Loans Better Than LendingClub?

Today I’m going to put it to the test, pitting it against the risk models of the very institution who issued those loans. That’s right, LendingClub included their own calculated loan grades (and sub-grades) in the dataset, so all the pieces are in place for the most thrilling risk modeling smackdown of this century (or at least this week). May the best algorithm win!

import joblib

prev_notebook_folder = "../input/building-a-neural-network-to-predict-loan-risk/"
loans = joblib.load(prev_notebook_folder + "loans_for_eval.joblib")
loans.shape

(1110171, 70)

loans.head()

┌────┬────────────┬────────────┬─────────────┬─────────────────┬─────────────┬─────────────────────┬────────┬──────────────┬───────────────────┬─────────────────┬──────┬────────────┬──────────────────┬────────────────────┬─────────────────┬─────────────────────────────┬─────────────────────┬───────────┬────────┬────────────┬─────────────────┐
│    │ loan_amnt  │   term     │ emp_length  │ home_ownership  │ annual_inc  │      purpose        │  dti   │ delinq_2yrs  │ cr_hist_age_mths  │ fico_range_low  │ ...  │ tax_liens  │ tot_hi_cred_lim  │ total_bal_ex_mort  │ total_bc_limit  │ total_il_high_credit_limit  │ fraction_recovered  │ issue_d   │ grade  │ sub_grade  │ expected_return │
├────┼────────────┼────────────┼─────────────┼─────────────────┼─────────────┼─────────────────────┼────────┼──────────────┼───────────────────┼─────────────────┼──────┼────────────┼──────────────────┼────────────────────┼─────────────────┼─────────────────────────────┼─────────────────────┼───────────┼────────┼────────────┼─────────────────┤
│ 0  │    3600.0  │ 36 months  │ 10+ years   │ MORTGAGE        │    55000.0  │ debt_consolidation  │  5.91  │         0.0  │            148.0  │          675.0  │ ...  │       0.0  │        178050.0  │            7746.0  │         2400.0  │                    13734.0  │                1.0  │ Dec-2015  │ C      │ C4         │         4429.08 │
│ 1  │   24700.0  │ 36 months  │ 10+ years   │ MORTGAGE        │    65000.0  │ small_business      │ 16.06  │         1.0  │            192.0  │          715.0  │ ...  │       0.0  │        314017.0  │           39475.0  │        79300.0  │                    24667.0  │                1.0  │ Dec-2015  │ C      │ C1         │        29530.08 │
│ 2  │   20000.0  │ 60 months  │ 10+ years   │ MORTGAGE        │    63000.0  │ home_improvement    │ 10.78  │         0.0  │            184.0  │          695.0  │ ...  │       0.0  │        218418.0  │           18696.0  │         6200.0  │                    14877.0  │                1.0  │ Dec-2015  │ B      │ B4         │        25959.60 │
│ 4  │   10400.0  │ 60 months  │ 3 years     │ MORTGAGE        │   104433.0  │ major_purchase      │ 25.37  │         1.0  │            210.0  │          695.0  │ ...  │       0.0  │        439570.0  │           95768.0  │        20300.0  │                    88097.0  │                1.0  │ Dec-2015  │ F      │ F1         │        17394.60 │
│ 5  │   11950.0  │ 36 months  │ 4 years     │ RENT            │    34000.0  │ debt_consolidation  │ 10.20  │         0.0  │            338.0  │          690.0  │ ...  │       0.0  │         16900.0  │           12798.0  │         9400.0  │                     4000.0  │                1.0  │ Dec-2015  │ C      │ C3         │        14586.48 │
└────┴────────────┴────────────┴─────────────┴─────────────────┴─────────────┴─────────────────────┴────────┴──────────────┴───────────────────┴─────────────────┴──────┴────────────┴──────────────────┴────────────────────┴─────────────────┴─────────────────────────────┴─────────────────────┴───────────┴────────┴────────────┴─────────────────┘
5 rows × 70 columns

This post was adapted from a Jupyter Notebook, by the way, so if you’d like to follow along in your own notebook, go ahead and fork mine Kaggle or GitHub!

Ground rules

This is going to be a clean fight—my model won’t use any data LendingClub wouldn’t have access to at the point they calculate a loan’s grade (including the grade itself).

I’m going to sort the dataset chronologically (using the

issue_d

column, the month and year the loan was issued) and split it into two parts. The first 80% I’ll use for training my competition model, and I’ll compare performance on the last 20%.

from sklearn.model_selection import train_test_split

loans["date"] = loans["issue_d"].astype("datetime64[ns]")
loans.sort_values("date", axis="index", inplace=True, kind="mergesort")

train, test = train_test_split(loans, test_size=0.2, shuffle=False)
train, test = train.copy(), test.copy()
print(f"The test set contains {len(test):,} loans.")

The test set contains 222,035 loans.

At the earlier end of the test set my model may have a slight informational advantage, having been trained on a few loans that may not have closed yet at the point LendingClub was grading those ones. On the other hand, LendingClub may have a slight informational advantage on the later end of the test set, since they would have known the outcomes of some loans on the earlier end of the test set by that time.

I have to give credit to Michael Wurm, by the way, for the idea of comparing my model’s performance to LendingClub’s loan grades, but my approach is pretty different. I’m not trying to simulate the performance of an investment portfolio; I’m just evaluating how well my predictions of simple risk compare.

Test metric

The test: who can pick the best set of grade A loans, judged on the basis of the independent variable from my last notebook, the fraction of an expected loan return that a prospective borrower will pay back (which I engineered as

fraction_recovered

LendingClub will take the plate first. I’ll gather all their grade A loans from the test set, count them, and calculate their average

fraction_recovered

. That average will be the metric my model has to beat.

Then I’ll train my model on the training set using the same pipeline and parameters I settled on in my last notebook. Once it’s trained, I’ll use it to make predictions on the test set, then gather the number of top predictions equal to the number of LendingClub’s grade A loans. Finally, I’ll calculate the same average of

fraction_recovered

on that subset, and we’ll have ourselves a winner!

LendingClub's turn

from statistics import mean

lc_grade_a = test[test["grade"] == "A"]
print(f"LendingClub gave {len(lc_grade_a):,} loans in the test set an A grade.")

print("\nAverage `fraction_recovered` on LendingClub's grade A loans:")
print(round(mean(lc_grade_a["fraction_recovered"]), 5))

LendingClub gave 38,779 loans in the test set an A grade.

Average `fraction_recovered` on LendingClub's grade A loans:
0.96021

That’s a pretty high percentage. I’m a bit nervous.

My turn

First, I’ll copy over my

run_pipeline

function from my previous notebook:

from sklearn.model_selection import train_test_split
from sklearn_pandas import DataFrameMapper
from sklearn.preprocessing import OneHotEncoder, OrdinalEncoder, StandardScaler
from tensorflow.keras import Sequential, Input
from tensorflow.keras.layers import Dense, Dropout


def run_pipeline(
    data,
    onehot_cols,
    ordinal_cols,
    batch_size,
    validate=True,
):
    X = data.drop(columns=["fraction_recovered"])
    y = data["fraction_recovered"]
    X_train, X_valid, y_train, y_valid = (
        train_test_split(X, y, test_size=0.2, random_state=0)
        if validate
        else (X, None, y, None)
    )

    transformer = DataFrameMapper(
        [
            (onehot_cols, OneHotEncoder(drop="if_binary")),
            (
                list(ordinal_cols.keys()),
                OrdinalEncoder(categories=list(ordinal_cols.values())),
            ),
        ],
        default=StandardScaler(),
    )

    X_train = transformer.fit_transform(X_train)
    X_valid = transformer.transform(X_valid) if validate else None

    input_nodes = X_train.shape[1]
    output_nodes = 1

    model = Sequential()
    model.add(Input((input_nodes,)))
    model.add(Dense(64, activation="relu"))
    model.add(Dropout(0.3, seed=0))
    model.add(Dense(32, activation="relu"))
    model.add(Dropout(0.3, seed=1))
    model.add(Dense(16, activation="relu"))
    model.add(Dropout(0.3, seed=2))
    model.add(Dense(output_nodes))
    model.compile(optimizer="adam", loss="mean_squared_logarithmic_error")

    history = model.fit(
        X_train,
        y_train,
        batch_size=batch_size,
        epochs=100,
        validation_data=(X_valid, y_valid) if validate else None,
        verbose=2,
    )

    return history.history, model, transformer


onehot_cols = ["term", "application_type", "home_ownership", "purpose"]
ordinal_cols = {
    "emp_length": [
        "< 1 year",
        "1 year",
        "2 years",
        "3 years",
        "4 years",
        "5 years",
        "6 years",
        "7 years",
        "8 years",
        "9 years",
        "10+ years",
    ]
}

Now for the moment of truth:

# Train the model
_, model, transformer = run_pipeline(
    train.drop(columns=["issue_d", "date", "grade", "sub_grade", "expected_return"]),
    onehot_cols,
    ordinal_cols,
    batch_size=128,
    validate=False,
)

# Make predictions
X_test = transformer.transform(
    test.drop(
        columns=[
            "fraction_recovered",
            "issue_d",
            "date",
            "grade",
            "sub_grade",
            "expected_return",
        ]
    )
)
test["model_predictions"] = model.predict(X_test)

# Gather top predictions
test_sorted = test.sort_values("model_predictions", axis="index", ascending=False)
ty_grade_a = test_sorted.iloc[0:len(lc_grade_a)]

# Display results
print("\nAverage `fraction_recovered` on Ty's grade A loans:")
print(format(mean(ty_grade_a["fraction_recovered"]), ".5f"))

Epoch 1/100
6939/6939 - 13s - loss: 0.0249
Epoch 2/100
6939/6939 - 13s - loss: 0.0204
Epoch 3/100
6939/6939 - 13s - loss: 0.0202
Epoch 4/100
6939/6939 - 13s - loss: 0.0202
Epoch 5/100
6939/6939 - 13s - loss: 0.0202
Epoch 6/100
6939/6939 - 14s - loss: 0.0201
Epoch 7/100
6939/6939 - 14s - loss: 0.0201
Epoch 8/100
6939/6939 - 14s - loss: 0.0201
Epoch 9/100
6939/6939 - 13s - loss: 0.0201
Epoch 10/100
6939/6939 - 12s - loss: 0.0201
Epoch 11/100
6939/6939 - 13s - loss: 0.0201
Epoch 12/100
6939/6939 - 13s - loss: 0.0201
Epoch 13/100
6939/6939 - 13s - loss: 0.0201
Epoch 14/100
6939/6939 - 13s - loss: 0.0201
Epoch 15/100
6939/6939 - 12s - loss: 0.0201
Epoch 16/100
6939/6939 - 12s - loss: 0.0201
Epoch 17/100
6939/6939 - 13s - loss: 0.0200
Epoch 18/100
6939/6939 - 13s - loss: 0.0200
Epoch 19/100
6939/6939 - 13s - loss: 0.0200
Epoch 20/100
6939/6939 - 14s - loss: 0.0200
Epoch 21/100
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Epoch 22/100
6939/6939 - 13s - loss: 0.0200
Epoch 23/100
6939/6939 - 12s - loss: 0.0200
Epoch 24/100
6939/6939 - 12s - loss: 0.0200
Epoch 25/100
6939/6939 - 12s - loss: 0.0200
Epoch 26/100
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Epoch 27/100
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Epoch 28/100
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Epoch 29/100
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Epoch 30/100
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Epoch 31/100
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Epoch 32/100
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Epoch 33/100
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Epoch 34/100
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Epoch 35/100
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Epoch 36/100
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Epoch 37/100
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Epoch 38/100
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Epoch 39/100
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Epoch 40/100
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Epoch 41/100
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Epoch 42/100
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Epoch 43/100
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Epoch 44/100
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Epoch 45/100
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Epoch 46/100
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Epoch 47/100
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Epoch 48/100
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Epoch 49/100
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Epoch 50/100
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Epoch 51/100
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Epoch 52/100
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Epoch 53/100
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Epoch 54/100
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Epoch 55/100
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Epoch 56/100
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Epoch 57/100
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Epoch 58/100
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Epoch 59/100
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Epoch 60/100
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Epoch 61/100
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Epoch 62/100
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Epoch 63/100
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Epoch 64/100
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Epoch 65/100
6939/6939 - 12s - loss: 0.0200
Epoch 66/100
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Epoch 67/100
6939/6939 - 14s - loss: 0.0200
Epoch 68/100
6939/6939 - 13s - loss: 0.0200
Epoch 69/100
6939/6939 - 13s - loss: 0.0200
Epoch 70/100
6939/6939 - 13s - loss: 0.0200
Epoch 71/100
6939/6939 - 13s - loss: 0.0200
Epoch 72/100
6939/6939 - 13s - loss: 0.0200
Epoch 73/100
6939/6939 - 13s - loss: 0.0200
Epoch 74/100
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Epoch 75/100
6939/6939 - 13s - loss: 0.0200
Epoch 76/100
6939/6939 - 13s - loss: 0.0200
Epoch 77/100
6939/6939 - 13s - loss: 0.0200
Epoch 78/100
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Epoch 79/100
6939/6939 - 14s - loss: 0.0200
Epoch 80/100
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Epoch 81/100
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Epoch 82/100
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Epoch 83/100
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Epoch 84/100
6939/6939 - 12s - loss: 0.0200
Epoch 85/100
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Epoch 86/100
6939/6939 - 13s - loss: 0.0200
Epoch 87/100
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Epoch 88/100
6939/6939 - 13s - loss: 0.0200
Epoch 89/100
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Epoch 90/100
6939/6939 - 13s - loss: 0.0200
Epoch 91/100
6939/6939 - 14s - loss: 0.0200
Epoch 92/100
6939/6939 - 13s - loss: 0.0200
Epoch 93/100
6939/6939 - 13s - loss: 0.0200
Epoch 94/100
6939/6939 - 13s - loss: 0.0200
Epoch 95/100
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Epoch 96/100
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Epoch 97/100
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Epoch 98/100
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Epoch 99/100
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Epoch 100/100
6939/6939 - 13s - loss: 0.0200

Average `fraction_recovered` on Ty's grade A loans:
0.96166

Victory!

Phew, that was a close one! My win might be too small to be statistically significant, but hey, it’s cool seeing that I can keep up with LendingClub’s best and brightest.

What I’d really like to know now is what quantitative range of estimated risk each LendingClub grade and sub-grade corresponds to, but it looks like that’s proprietary. Does anyone know if loans grades generally correspond to certain percentage ranges like letter grades in academic classes? If not, have any ideas for better benchmarks I could use to evaluate my model’s performance? Go ahead and chime in in the comments below.

Previously published at https://tymick.me/blog/loan-grading-showdown