Building a Fraud Prediction Model with EvalML

In this demo, we will build an optimized fraud prediction model using EvalML. To optimize the pipeline, we will set up an objective function to minimize the percentage of total transaction value lost to fraud. At the end of this demo, we also show you how introducing the right objective during the training is over 4x better than using a generic machine learning metric like AUC.

[1]:
import evalml
from evalml import AutoMLSearch
from evalml.objectives import FraudCost

Configure “Cost of Fraud”

To optimize the pipelines toward the specific business needs of this model, you can set your own assumptions for the cost of fraud. These parameters are

  • retry_percentage - what percentage of customers will retry a transaction if it is declined?

  • interchange_fee - how much of each successful transaction do you collect?

  • fraud_payout_percentage - the percentage of fraud will you be unable to collect

  • amount_col - the column in the data the represents the transaction amount

Using these parameters, EvalML determines attempt to build a pipeline that will minimize the financial loss due to fraud.

[2]:
fraud_objective = FraudCost(retry_percentage=.5,
                            interchange_fee=.02,
                            fraud_payout_percentage=.75,
                            amount_col='amount')

Search for best pipeline

In order to validate the results of the pipeline creation and optimization process, we will save some of our data as a holdout set

[3]:
X, y = evalml.demos.load_fraud(n_rows=2500)
             Number of Features
Boolean                       1
Categorical                   6
Numeric                       5

Number of training examples: 2500
Targets
False    85.92%
True     14.08%
Name: fraud, dtype: object

EvalML natively supports one-hot encoding. Here we keep 1 out of the 6 categorical columns to decrease computation time.

[4]:
X = X.drop(['datetime', 'expiration_date', 'country', 'region', 'provider'], axis=1)

X_train, X_holdout, y_train, y_holdout = evalml.preprocessing.split_data(X, y, test_size=0.2, random_state=0)

print(X.dtypes)
card_id               int64
store_id              int64
amount                int64
currency             object
customer_present       bool
lat                 float64
lng                 float64
dtype: object

Because the fraud labels are binary, we will use AutoMLSearch(problem_type='binary'). When we call .search(), the search for the best pipeline will begin.

[5]:
automl = AutoMLSearch(problem_type='binary',
                      objective=fraud_objective,
                      additional_objectives=['auc', 'f1', 'precision'],
                      max_batches=1,
                      optimize_thresholds=True)

automl.search(X_train, y_train)
`X` passed was not a DataTable. EvalML will try to convert the input as a Woodwork DataTable and types will be inferred. To control this behavior, please pass in a Woodwork DataTable instead.
`y` passed was not a DataColumn. EvalML will try to convert the input as a Woodwork DataTable and types will be inferred. To control this behavior, please pass in a Woodwork DataTable instead.
Generating pipelines to search over...
*****************************
* Beginning pipeline search *
*****************************

Optimizing for Fraud Cost.
Lower score is better.

Searching up to 1 batches for a total of 9 pipelines.
Allowed model families: lightgbm, catboost, extra_trees, random_forest, xgboost, linear_model, decision_tree

Batch 1: (1/9) Mode Baseline Binary Classification P... Elapsed:00:00
        Starting cross validation
        Finished cross validation - mean Fraud Cost: 0.029
Batch 1: (2/9) Decision Tree Classifier w/ Imputer +... Elapsed:00:00
        Starting cross validation
        Finished cross validation - mean Fraud Cost: 0.003
High coefficient of variation (cv >= 0.2) within cross validation scores. Decision Tree Classifier w/ Imputer + One Hot Encoder may not perform as estimated on unseen data.
Batch 1: (3/9) LightGBM Classifier w/ Imputer + One ... Elapsed:00:02
        Starting cross validation
        Finished cross validation - mean Fraud Cost: 0.016
High coefficient of variation (cv >= 0.2) within cross validation scores. LightGBM Classifier w/ Imputer + One Hot Encoder may not perform as estimated on unseen data.
Batch 1: (4/9) Extra Trees Classifier w/ Imputer + O... Elapsed:00:03
        Starting cross validation
        Finished cross validation - mean Fraud Cost: 0.002
Batch 1: (5/9) Elastic Net Classifier w/ Imputer + O... Elapsed:00:06
        Starting cross validation
        Finished cross validation - mean Fraud Cost: 0.002
Batch 1: (6/9) CatBoost Classifier w/ Imputer           Elapsed:00:07
        Starting cross validation
        Finished cross validation - mean Fraud Cost: 0.002
Batch 1: (7/9) XGBoost Classifier w/ Imputer + One H... Elapsed:00:09
        Starting cross validation
        Finished cross validation - mean Fraud Cost: 0.002
Batch 1: (8/9) Random Forest Classifier w/ Imputer +... Elapsed:00:10
        Starting cross validation
        Finished cross validation - mean Fraud Cost: 0.002
Batch 1: (9/9) Logistic Regression Classifier w/ Imp... Elapsed:00:13
        Starting cross validation
        Finished cross validation - mean Fraud Cost: 0.002

Search finished after 00:16
Best pipeline: Random Forest Classifier w/ Imputer + One Hot Encoder
Best pipeline Fraud Cost: 0.002224

View rankings and select pipeline

Once the fitting process is done, we can see all of the pipelines that were searched, ranked by their score on the fraud detection objective we defined

[6]:
automl.rankings
[6]:
id pipeline_name score validation_score percent_better_than_baseline high_variance_cv parameters
0 7 Random Forest Classifier w/ Imputer + One Hot ... 0.002224 0.002160 92.420088 False {'Imputer': {'categorical_impute_strategy': 'm...
1 8 Logistic Regression Classifier w/ Imputer + On... 0.002231 0.002025 92.396719 False {'Imputer': {'categorical_impute_strategy': 'm...
2 6 XGBoost Classifier w/ Imputer + One Hot Encoder 0.002233 0.002081 92.389841 False {'Imputer': {'categorical_impute_strategy': 'm...
3 3 Extra Trees Classifier w/ Imputer + One Hot En... 0.002255 0.002391 92.312932 False {'Imputer': {'categorical_impute_strategy': 'm...
4 5 CatBoost Classifier w/ Imputer 0.002266 0.002107 92.274517 False {'Imputer': {'categorical_impute_strategy': 'm...
5 4 Elastic Net Classifier w/ Imputer + One Hot En... 0.002276 0.002284 92.240896 False {'Imputer': {'categorical_impute_strategy': 'm...
6 1 Decision Tree Classifier w/ Imputer + One Hot ... 0.003290 0.002710 88.784911 True {'Imputer': {'categorical_impute_strategy': 'm...
7 2 LightGBM Classifier w/ Imputer + One Hot Encoder 0.015661 0.033936 46.619411 True {'Imputer': {'categorical_impute_strategy': 'm...
8 0 Mode Baseline Binary Classification Pipeline 0.029337 0.023497 0.000000 False {'Baseline Classifier': {'strategy': 'mode'}}

to select the best pipeline we can run

[7]:
best_pipeline = automl.best_pipeline

Describe pipeline

You can get more details about any pipeline. Including how it performed on other objective functions.

[8]:
automl.describe_pipeline(automl.rankings.iloc[1]["id"])
*********************************************************************************
* Logistic Regression Classifier w/ Imputer + One Hot Encoder + Standard Scaler *
*********************************************************************************

Problem Type: binary
Model Family: Linear

Pipeline Steps
==============
1. Imputer
         * categorical_impute_strategy : most_frequent
         * numeric_impute_strategy : mean
         * categorical_fill_value : None
         * numeric_fill_value : None
2. One Hot Encoder
         * top_n : 10
         * features_to_encode : None
         * categories : None
         * drop : None
         * handle_unknown : ignore
         * handle_missing : error
3. Standard Scaler
4. Logistic Regression Classifier
         * penalty : l2
         * C : 1.0
         * n_jobs : -1
         * multi_class : auto
         * solver : lbfgs

Training
========
Training for binary problems.
Objective to optimize binary classification pipeline thresholds for: <evalml.objectives.fraud_cost.FraudCost object at 0x7f09f04b7050>
Total training time (including CV): 2.7 seconds

Cross Validation
----------------
             Fraud Cost   AUC    F1  Precision # Training # Testing
0                 0.002 0.804 0.247      0.141   1066.000   667.000
1                 0.002 0.821 0.247      0.141   1066.000   667.000
2                 0.002 0.797 0.247      0.141   1067.000   666.000
mean              0.002 0.807 0.247      0.141          -         -
std               0.000 0.012 0.000      0.000          -         -
coef of var       0.086 0.015 0.001      0.001          -         -

Evaluate on hold out

Finally, we retrain the best pipeline on all of the training data and evaluate on the holdout

[9]:
best_pipeline.fit(X_train, y_train)
[9]:
GeneratedPipeline(parameters={'Imputer':{'categorical_impute_strategy': 'most_frequent', 'numeric_impute_strategy': 'mean', 'categorical_fill_value': None, 'numeric_fill_value': None}, 'One Hot Encoder':{'top_n': 10, 'features_to_encode': None, 'categories': None, 'drop': None, 'handle_unknown': 'ignore', 'handle_missing': 'error'}, 'Random Forest Classifier':{'n_estimators': 100, 'max_depth': 6, 'n_jobs': -1},})

Now, we can score the pipeline on the hold out data using both the fraud cost score and the AUC.

[10]:
best_pipeline.score(X_holdout, y_holdout, objectives=["auc", fraud_objective])
[10]:
OrderedDict([('AUC', 0.8370764119601328),
             ('Fraud Cost', 0.004329350526560073)])

Why optimize for a problem-specific objective?

To demonstrate the importance of optimizing for the right objective, let’s search for another pipeline using AUC, a common machine learning metric. After that, we will score the holdout data using the fraud cost objective to see how the best pipelines compare.

[11]:
automl_auc = AutoMLSearch(problem_type='binary',
                          objective='auc',
                          additional_objectives=['f1', 'precision'],
                          max_batches=1,
                          optimize_thresholds=True)

automl_auc.search(X_train, y_train)
`X` passed was not a DataTable. EvalML will try to convert the input as a Woodwork DataTable and types will be inferred. To control this behavior, please pass in a Woodwork DataTable instead.
`y` passed was not a DataColumn. EvalML will try to convert the input as a Woodwork DataTable and types will be inferred. To control this behavior, please pass in a Woodwork DataTable instead.
Generating pipelines to search over...
*****************************
* Beginning pipeline search *
*****************************

Optimizing for AUC.
Greater score is better.

Searching up to 1 batches for a total of 9 pipelines.
Allowed model families: lightgbm, catboost, extra_trees, random_forest, xgboost, linear_model, decision_tree

Batch 1: (1/9) Mode Baseline Binary Classification P... Elapsed:00:00
        Starting cross validation
        Finished cross validation - mean AUC: 0.500
Batch 1: (2/9) Decision Tree Classifier w/ Imputer +... Elapsed:00:00
        Starting cross validation
        Finished cross validation - mean AUC: 0.816
Batch 1: (3/9) LightGBM Classifier w/ Imputer + One ... Elapsed:00:00
        Starting cross validation
        Finished cross validation - mean AUC: 0.862
Batch 1: (4/9) Extra Trees Classifier w/ Imputer + O... Elapsed:00:01
        Starting cross validation
        Finished cross validation - mean AUC: 0.812
Batch 1: (5/9) Elastic Net Classifier w/ Imputer + O... Elapsed:00:03
        Starting cross validation
        Finished cross validation - mean AUC: 0.500
Batch 1: (6/9) CatBoost Classifier w/ Imputer           Elapsed:00:04
        Starting cross validation
        Finished cross validation - mean AUC: 0.826
Batch 1: (7/9) XGBoost Classifier w/ Imputer + One H... Elapsed:00:04
        Starting cross validation
        Finished cross validation - mean AUC: 0.857
Batch 1: (8/9) Random Forest Classifier w/ Imputer +... Elapsed:00:05
        Starting cross validation
        Finished cross validation - mean AUC: 0.855
Batch 1: (9/9) Logistic Regression Classifier w/ Imp... Elapsed:00:07
        Starting cross validation
        Finished cross validation - mean AUC: 0.805

Search finished after 00:08
Best pipeline: LightGBM Classifier w/ Imputer + One Hot Encoder
Best pipeline AUC: 0.861867

like before, we can look at the rankings and pick the best pipeline

[12]:
automl_auc.rankings
[12]:
id pipeline_name score validation_score percent_better_than_baseline high_variance_cv parameters
0 2 LightGBM Classifier w/ Imputer + One Hot Encoder 0.861867 0.859753 72.373388 False {'Imputer': {'categorical_impute_strategy': 'm...
1 6 XGBoost Classifier w/ Imputer + One Hot Encoder 0.857384 0.870892 71.476771 False {'Imputer': {'categorical_impute_strategy': 'm...
2 7 Random Forest Classifier w/ Imputer + One Hot ... 0.855245 0.886692 71.049065 False {'Imputer': {'categorical_impute_strategy': 'm...
3 5 CatBoost Classifier w/ Imputer 0.825871 0.836211 65.174100 False {'Imputer': {'categorical_impute_strategy': 'm...
4 1 Decision Tree Classifier w/ Imputer + One Hot ... 0.815774 0.803489 63.154821 False {'Imputer': {'categorical_impute_strategy': 'm...
5 3 Extra Trees Classifier w/ Imputer + One Hot En... 0.812146 0.794475 62.429102 False {'Imputer': {'categorical_impute_strategy': 'm...
6 8 Logistic Regression Classifier w/ Imputer + On... 0.805033 0.790465 61.006542 False {'Imputer': {'categorical_impute_strategy': 'm...
7 0 Mode Baseline Binary Classification Pipeline 0.500000 0.500000 0.000000 False {'Baseline Classifier': {'strategy': 'mode'}}
8 4 Elastic Net Classifier w/ Imputer + One Hot En... 0.500000 0.500000 0.000000 False {'Imputer': {'categorical_impute_strategy': 'm...
[13]:
best_pipeline_auc = automl_auc.best_pipeline

# train on the full training data
best_pipeline_auc.fit(X_train, y_train)
[13]:
GeneratedPipeline(parameters={'Imputer':{'categorical_impute_strategy': 'most_frequent', 'numeric_impute_strategy': 'mean', 'categorical_fill_value': None, 'numeric_fill_value': None}, 'One Hot Encoder':{'top_n': 10, 'features_to_encode': None, 'categories': None, 'drop': None, 'handle_unknown': 'ignore', 'handle_missing': 'error'}, 'LightGBM Classifier':{'boosting_type': 'gbdt', 'learning_rate': 0.1, 'n_estimators': 100, 'max_depth': 0, 'num_leaves': 31, 'min_child_samples': 20, 'n_jobs': -1, 'bagging_freq': 0, 'bagging_fraction': 0.9},})
[14]:
# get the fraud score on holdout data
best_pipeline_auc.score(X_holdout, y_holdout,  objectives=["auc", fraud_objective])
[14]:
OrderedDict([('AUC', 0.8441860465116278),
             ('Fraud Cost', 0.004348876090615287)])
[15]:
# fraud score on fraud optimized again
best_pipeline.score(X_holdout, y_holdout, objectives=["auc", fraud_objective])
[15]:
OrderedDict([('AUC', 0.8370764119601328),
             ('Fraud Cost', 0.004329350526560073)])

When we optimize for AUC, we can see that the AUC score from this pipeline is better than the AUC score from the pipeline optimized for fraud cost. However, the losses due to fraud are over 3% of the total transaction amount when optimized for AUC and under 1% when optimized for fraud cost. As a result, we lose more than 2% of the total transaction amount by not optimizing for fraud cost specifically.

This happens because optimizing for AUC does not take into account the user-specified retry_percentage, interchange_fee, fraud_payout_percentage values. Thus, the best pipelines may produce the highest AUC but may not actually reduce the amount loss due to your specific type fraud.

This example highlights how performance in the real world can diverge greatly from machine learning metrics.