# 4. Sequential models and representation learning¶

In credit card fraud detection, the choice of features is crucial for accurate classification. Considerable research effort has been dedicated to the development of features that are relevant to characterize fraudulent patterns. Feature engineering strategies based on feature aggregations have become commonly adopted [BASO16]. Feature aggregation establishes a connection between the current transaction and other related transactions by computing statistics over their variables. They often originate from complex rules defined from human expertise, and many experiments report that they significantly improve detection .

Using feature aggregation for fraud detection is part of a research topic referred to as “context-aware fraud detection”, where one considers the context (e.g. the cardholder history) associated with a transaction to make the decision. This allows, for instance, to give insights into the properties of the transaction relative to the usual purchase patterns of the cardholder and/or terminal, which is intuitively a relevant piece of information.

## 4.1. Context-aware fraud detection¶

The context is established based on a landmark variable, which is in most of the cases the Customer ID. Concretely, one starts by building the sequence of historical transactions, chronologically ordered from the oldest to the current one, that have the same value for the landmark variable as the current transaction.

This whole sequence is the raw basis for context-aware approaches. Indeed, the general process relies on the construction of new features or representations for each given transaction based on its contextual sequence. However, approaches can be divided into two broad categories, the first (Expert representations) relying on domain knowledge from experts to create rules and build feature aggregation, and the second (Automatic representations) oriented towards automated feature extraction strategies with deep learning models.

### 4.1.1. Expert representations¶

To build expert features from the base sequence, a selection layer relying on expert constraints (same Merchant Category code, same Country, more recent than a week, etc.) is first applied to obtain a more specific subsequence. Then, an aggregation function (sum, avg, …) is computed over the values of a chosen feature (e.g. amount) in the subsequence. For more details, [BASO16] provides a formal definition of such feature aggregations. Expert features are not necessarily transferable in any fraud detection domain since they rely on specific features that might not always be available. Nevertheless, in practice, the landmark features, constraints features, and aggregated features are often chosen from a set of frequent attributes, comprising Time, Amount, Country, Merchant Category, Customer ID, transaction type.

### 4.1.2. Automatic representations¶

The other family of context-aware approaches considers the sequence directly as input in a model and lets it automatically learn the right connections to optimize fraud detection. The advantage of not relying on human knowledge to build the relevant features is obviously to save the costly resources and to ease adaptability and maintenance. Moreover, models can be pushed towards large architectures and learn very complex variable relationships automatically from data. However, it requires a sufficiently large dataset to properly identify the relevant patterns. Otherwise, the feature representations are not very accurate or useful.

A baseline technique for automatic usage of contextual data would be to exploit the transaction sequence in its entirety (e.g. flattening it into a set of features), but this removes the information about the order and could lead to a very high dimensional feature space. A more popular strategy consists of (1) turning the sequences into fixed-size sequences and (2) using a special family of models that are able to deal with sequences naturally and summarizing them into relevant vector representations for fraud classification.

This strategy is often referred to as sequential learning, the study of learning algorithms for sequential data. The sequential dependency between data points is learned at the algorithmic level. This includes sliding window methods, which often tend to ignore the order between data points within the window, but also models that are designed explicitly to consider the sequential order between consecutive data points (e.g. a Markov Chain). Such models can be found in the family of deep learning architectures under the recurrent neural networks category. The link between consecutive elements of the sequence is embedded in the design of the recurrent architecture, where the computation of the hidden state/layer of a more recent event depends on hidden states of previous events.

Sliding window methods include architectures like 1D convolutional neural networks (1D-CNN), and sequential methods include architectures like the long short-term memory (LSTM). Such architectures have proven to be very efficient in the context of fraud detection in the past [JGZ+18].

In this section, the goal is to explore automatic representation learning from cardholders’ sequences of transactions and its application in context-aware fraud detection. The content starts with the practical aspects of building the pipeline to manage sequential data. Then, three architectures are successively explored: a 1D-CNN, an LSTM, and a more complex model that uses an LSTM with Attention [BCB14]. The section concludes on perspectives of other modeling possibilities such as sequence-to-sequence autoencoders [AHJ+20, SVL14], or other combinations of the explored models.

## 4.2. Data processing¶

With sequence modeling, the building of the data processing pipeline has special importance. In particular, as explained in the introduction, its role is to create the input sequences for the sequential models to learn the representations.

The most popular landmark variable to establish the sequence is the Customer ID. Indeed, by the very definition of credit card fraud being a payment done by someone other than the cardholder, it makes the most sense to look at the history of the customer in order to determine when a card payment is fraudulent.

Therefore, the goal here is to establish Datasets and DataLoaders that provide, given a transaction index in the dataset, the sequence of previous transactions (including the one referred by the index) from the same cardholder. Moreover, since models usually deal with fixed-sized sequences, the sequence length will be a parameter, and sequences that are too long (resp. too short) will be cut (resp. padded).

As usual, let us start by loading a fixed training and validation period from the processed dataset.

# Initialization: Load shared functions and simulated data

!curl -O https://raw.githubusercontent.com/Fraud-Detection-Handbook/fraud-detection-handbook/main/Chapter_References/shared_functions.py
%run shared_functions.py

# Get simulated data from Github repository
if not os.path.exists("simulated-data-transformed"):
!git clone https://github.com/Fraud-Detection-Handbook/simulated-data-transformed

  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
100 63257  100 63257    0     0   537k      0 --:--:-- --:--:-- --:--:--  532k

DIR_INPUT='simulated-data-transformed/data/'

BEGIN_DATE = "2018-06-11"
END_DATE = "2018-09-14"

print("{0} transactions loaded, containing {1} fraudulent transactions".format(len(transactions_df),transactions_df.TX_FRAUD.sum()))

output_feature="TX_FRAUD"

input_features=['TX_AMOUNT','TX_DURING_WEEKEND', 'TX_DURING_NIGHT', 'CUSTOMER_ID_NB_TX_1DAY_WINDOW',
'CUSTOMER_ID_AVG_AMOUNT_1DAY_WINDOW', 'CUSTOMER_ID_NB_TX_7DAY_WINDOW',
'CUSTOMER_ID_AVG_AMOUNT_7DAY_WINDOW', 'CUSTOMER_ID_NB_TX_30DAY_WINDOW',
'CUSTOMER_ID_AVG_AMOUNT_30DAY_WINDOW', 'TERMINAL_ID_NB_TX_1DAY_WINDOW',
'TERMINAL_ID_RISK_1DAY_WINDOW', 'TERMINAL_ID_NB_TX_7DAY_WINDOW',
'TERMINAL_ID_RISK_7DAY_WINDOW', 'TERMINAL_ID_NB_TX_30DAY_WINDOW',
'TERMINAL_ID_RISK_30DAY_WINDOW']

Load  files
CPU times: user 311 ms, sys: 237 ms, total: 548 ms
Wall time: 570 ms
919767 transactions loaded, containing 8195 fraudulent transactions

# Set the starting day for the training period, and the deltas
start_date_training = datetime.datetime.strptime("2018-07-25", "%Y-%m-%d")
delta_train=7
delta_delay=7
delta_test=7

delta_valid = delta_test

start_date_training_with_valid = start_date_training+datetime.timedelta(days=-(delta_delay+delta_valid))

(train_df, valid_df)=get_train_test_set(transactions_df,start_date_training_with_valid,
delta_train=delta_train,delta_delay=delta_delay,delta_test=delta_test)

# By default, scales input data
(train_df, valid_df)=scaleData(train_df, valid_df,input_features)

input_features

['TX_AMOUNT',
'TX_DURING_WEEKEND',
'TX_DURING_NIGHT',
'CUSTOMER_ID_NB_TX_1DAY_WINDOW',
'CUSTOMER_ID_AVG_AMOUNT_1DAY_WINDOW',
'CUSTOMER_ID_NB_TX_7DAY_WINDOW',
'CUSTOMER_ID_AVG_AMOUNT_7DAY_WINDOW',
'CUSTOMER_ID_NB_TX_30DAY_WINDOW',
'CUSTOMER_ID_AVG_AMOUNT_30DAY_WINDOW',
'TERMINAL_ID_NB_TX_1DAY_WINDOW',
'TERMINAL_ID_RISK_1DAY_WINDOW',
'TERMINAL_ID_NB_TX_7DAY_WINDOW',
'TERMINAL_ID_RISK_7DAY_WINDOW',
'TERMINAL_ID_NB_TX_30DAY_WINDOW',
'TERMINAL_ID_RISK_30DAY_WINDOW']


This time, additionally to the above features, building the sequences will require two additional fields from the DataFrames:

• CUSTOMER_ID: the landmark variable that will be used to select past transactions.

• TX_DATETIME: the time variable that will allow building sequences in chronological order.

dates = train_df['TX_DATETIME'].values

customer_ids = train_df['CUSTOMER_ID'].values


There are multiple ways to implement sequence creation for training/validation. One way is to precompute, for each transaction, the indices of the previous transactions of the sequence and to store them. Then, to build the sequences of features on the fly from the indices.

Here we propose some steps to proceed but keep in mind that other solutions are just as valid.

### 4.2.1. Setting the sequence length¶

The first step is to set a sequence length. In the literature, 5 or 10 are two values that are often chosen [JGZ+18]. This can later be a parameter to tune but here, let us arbitrarily set it to 5 to begin with.

seq_len = 5


### 4.2.2. Ordering elements chronologically¶

In our case, transactions are already sorted, but in the most general case, to build the sequences, it is necessary to sort all transactions chronologically and keep the sorting indices:

indices_sort = np.argsort(dates)
sorted_dates = dates[indices_sort]
sorted_ids = customer_ids[indices_sort]


### 4.2.3. Separating data according to the landmark variable (Customer ID)¶

After sorting, the dataset is a large sequence with all transactions from all cardholders. We can separate it into several sequences that each contain only the transactions of a single cardholder. Finally, each customer sequence can be turned into several fixed-size sequences using a sliding window and padding.

To separate the dataset, let us get the list of customers:

unique_customer_ids = np.unique(sorted_ids)

unique_customer_ids[0:10]

array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])


For each customer, the associated subsequence can be selected with a boolean mask. Here is for example the sequence of transaction IDs for customer 0.

idx = 0
current_customer_id = unique_customer_ids[idx]
# this is the full sequence of transaction indices (after sort) for customer 0
# this is the full sequence of transaction indices (before sort) for customer 0
customer_full_seq_original_indices = indices_sort[customer_full_seq]
customer_full_seq_original_indices

array([ 1888, 10080, 12847, 15627, 18908, 22842, 37972, 42529, 44495,
48980, 58692, 63977])


### 4.2.4. Turning a customer sequence into fixed size sequences¶

The above sequence is the whole sequence for customer 0. But the goal is to have, for each transaction i of this sequence, a fixed size sequence that ends with transaction i, which will be used as input in the sequential model to predict the label of transaction i. In the example above:

• For transaction 1888: the 4 previous transactions are [none,none,none,none] (Note: none can be replaced with a default value like -1). So the sequence [none, none, none, none, 1888] will be created.

• For transaction 10080, the sequence will be [none, none, none, 1888, 10080]

• For transaction 37972, the sequence will be [12847, 15627, 18908, 22842, 37972]

• Etc.

Using a sliding window (or rolling window) allows to obtain those sequences:

def rolling_window(array, window):
a = np.concatenate([np.ones((window-1,))*-1,array])
shape = a.shape[:-1] + (a.shape[-1] - window + 1, window)
strides = a.strides + (a.strides[-1],)
return np.lib.stride_tricks.as_strided(a, shape=shape, strides=strides).astype(int)

customer_all_seqs = rolling_window(customer_full_seq_original_indices,seq_len)

customer_all_seqs

array([[   -1,    -1,    -1,    -1,  1888],
[   -1,    -1,    -1,  1888, 10080],
[   -1,    -1,  1888, 10080, 12847],
[   -1,  1888, 10080, 12847, 15627],
[ 1888, 10080, 12847, 15627, 18908],
[10080, 12847, 15627, 18908, 22842],
[12847, 15627, 18908, 22842, 37972],
[15627, 18908, 22842, 37972, 42529],
[18908, 22842, 37972, 42529, 44495],
[22842, 37972, 42529, 44495, 48980],
[37972, 42529, 44495, 48980, 58692],
[42529, 44495, 48980, 58692, 63977]])


### 4.2.5. Generating the sequences of transaction features on the fly from the sequences of indices¶

From the indices’ sequences and the features of each transaction (available in x_train), building the features sequences is straightforward. Let us do it for the 6th sequence:

customer_all_seqs[5]

array([10080, 12847, 15627, 18908, 22842])

x_train = torch.FloatTensor(train_df[input_features].values)

sixth_sequence = x_train[customer_all_seqs[5],:]

sixth_sequence

tensor([[ 0.6965, -0.6306,  2.1808, -0.8466,  0.0336, -1.1665,  0.0176, -0.9341,
0.2310, -0.9810, -0.0816, -0.3445, -0.1231, -0.2491, -0.1436],
[ 0.0358, -0.6306, -0.4586, -0.8466,  0.4450, -1.1665,  0.1112, -0.8994,
0.2278,  0.0028, -0.0816,  0.6425, -0.1231, -0.0082, -0.1436],
[ 1.1437, -0.6306, -0.4586, -0.3003,  0.7595, -1.0352,  0.2462, -0.8994,
0.2458,  1.9702, -0.0816,  1.3005, -0.1231,  1.7989, -0.1436],
[ 0.3645, -0.6306, -0.4586,  0.2461,  0.6804, -1.0352,  0.3186, -0.8647,
0.2514,  1.9702, -0.0816,  0.3135, -0.1231, -0.8514, -0.1436],
[ 0.3348, -0.6306, -0.4586, -0.3003,  0.7462, -1.1665,  0.2494, -0.8994,
0.2262, -0.9810, -0.0816, -2.3185, -0.1231, -1.5743, -0.1436]])

sixth_sequence.shape

torch.Size([5, 15])


Note: Here, the sequence of indices (customer_all_seqs[5]) was made of valid indices. When there are invalid indices (-1), the idea is to put a “padding transaction” (e.g. with all features equal to zero or equal to the average value that they have in the training set) in the final sequence. To obtain a homogeneous code that can be used for both valid and invalid indices, on can append the “padding transaction” to x_train at the end and replace all -1 with the index of this added transaction.

### 4.2.6. Efficient implementation with pandas and groupby¶

The above steps are described for educational purposes as they allow to understand all the necessary operations to build the sequences of transactions. In practice, because this process requires a time-consuming loop over all Customer IDs, it is better to rely on a dataframe instead and use the pandas groupby function. More precisely, the idea is to group the elements of the transaction dataframe by Customer ID and to use the shift function to determine, for each transaction, the ones that occurred before. In order not to edit the original dataframe, let us first create a new one that only contains the necessary features.

df_ids_dates = pd.DataFrame({'CUSTOMER_ID': customer_ids,
'TX_DATETIME': dates})

#checking if the transaction are chronologically ordered
datetime_diff = (df_ids_dates["TX_DATETIME"] - df_ids_dates["TX_DATETIME"].shift(1)).iloc[1:].dt.total_seconds()
assert (datetime_diff >= 0).all()


Let us now add a new column with the initial row indices, that will be later used with the shift function.

df_ids_dates["tmp_index"]  = np.arange(len(df_ids_dates))

df_ids_dates.head()

CUSTOMER_ID TX_DATETIME tmp_index
0 579 2018-07-11 00:00:54 0
1 181 2018-07-11 00:01:59 1
2 4386 2018-07-11 00:03:39 2
3 4599 2018-07-11 00:05:50 3
4 4784 2018-07-11 00:06:04 4

The next step is to group the elements by Customer ID:

df_groupby_customer_id = df_ids_dates.groupby("CUSTOMER_ID")


Now it is possible to compute a shifted tmp_index with respect to the grouping by CUSTOMER_ID. For instance, shifting by 0 gives the current transaction index and shifting by 1 gives the previous transaction index (or NaN if the current transaction is the first transaction of the customer).

df_groupby_customer_id["tmp_index"].shift(0)

0            0
1            1
2            2
3            3
4            4
...
66923    66923
66924    66924
66925    66925
66926    66926
66927    66927
Name: tmp_index, Length: 66928, dtype: int64

df_groupby_customer_id["tmp_index"].shift(1)

0            NaN
1            NaN
2            NaN
3            NaN
4            NaN
...
66923    66805.0
66924    64441.0
66925    66777.0
66926    63338.0
66927    60393.0
Name: tmp_index, Length: 66928, dtype: float64


To obtain the whole sequences of indices, the only thing to do is to loop over the shift parameter, from seq_len - 1 to 0.

sequence_indices = pd.DataFrame(
{
"tx_{}".format(n): df_groupby_customer_id["tmp_index"].shift(seq_len - n - 1)
for n in range(seq_len)
}
)

sequence_indices = sequence_indices.fillna(-1).astype(int)

sequence_indices.head()

tx_0 tx_1 tx_2 tx_3 tx_4
0 -1 -1 -1 -1 0
1 -1 -1 -1 -1 1
2 -1 -1 -1 -1 2
3 -1 -1 -1 -1 3
4 -1 -1 -1 -1 4

As a sanity check, let us see if this method computes the same sequences as the previous method for transaction 12847, 15627 and 18908 which were (see 4.2.4):

• [ -1, -1, 1888, 10080, 12847]

• [ -1, 1888, 10080, 12847, 15627]

• [ 1888, 10080, 12847, 15627, 18908]

print(sequence_indices.loc[12847].values)
print(sequence_indices.loc[15627].values)
print(sequence_indices.loc[18908].values)

[   -1    -1  1888 10080 12847]
[   -1  1888 10080 12847 15627]
[ 1888 10080 12847 15627 18908]


### 4.2.7. Managing sequence creation into a torch Dataset¶

Now that the process is ready and tested, the final step is to implement it within a torch Dataset to use it in a training loop. To simplify the usage, let us consider the “zeros” padding strategy as default. The precomputation of indices and the creation of the padding transaction (a transaction with all features to zero) will be done at initialization. Then, the __getitem__ function will build the sequence of features on the fly.

if torch.cuda.is_available():
DEVICE = "cuda"
else:
DEVICE = "cpu"
print("Selected device is",DEVICE)

Selected device is cuda

class FraudSequenceDataset(torch.utils.data.Dataset):

def __init__(self, x,y,customer_ids, dates, seq_len, padding_mode = 'zeros', output=True):
'Initialization'

# x,y,customer_ids, and dates must have the same length

# storing the features x in self.features and adding the "padding" transaction at the end
self.features = torch.vstack([x, x.mean(axis=0)])
self.features = torch.vstack([x, torch.zeros(x[0,:].shape)])
else:
raise ValueError('padding_mode must be "mean" or "zeros"')
self.y = y
self.customer_ids = customer_ids
self.dates = dates
self.seq_len = seq_len
self.output = output

#===== computing sequences ids =====

df_ids_dates = pd.DataFrame({'CUSTOMER_ID':customer_ids,
'TX_DATETIME':dates})

df_ids_dates["tmp_index"]  = np.arange(len(df_ids_dates))
df_groupby_customer_id = df_ids_dates.groupby("CUSTOMER_ID")
sequence_indices = pd.DataFrame(
{
"tx_{}".format(n): df_groupby_customer_id["tmp_index"].shift(seq_len - n - 1)
for n in range(seq_len)
}
)

#replaces -1 (padding) with the index of the padding transaction (last index of self.features)
self.sequences_ids = sequence_indices.fillna(len(self.features) - 1).values.astype(int)

def __len__(self):
'Denotes the total number of samples'
return len(self.customer_ids)

def __getitem__(self, index):
'Generates one sample of data'
# Select sample index

tx_ids = self.sequences_ids[index]

if self.output:
#transposing because the CNN considers the channel dimension before the sequence dimension
return self.features[tx_ids,:].transpose(0,1).to(DEVICE), self.y[index].to(DEVICE)
else:
return self.features[tx_ids,:].transpose(0,1).to(DEVICE)


As a sanity check, let us try the Dataset within a DataLoader

x_train = torch.FloatTensor(train_df[input_features].values)
x_valid = torch.FloatTensor(valid_df[input_features].values)
y_train = torch.FloatTensor(train_df[output_feature].values)
y_valid = torch.FloatTensor(valid_df[output_feature].values)

SEED = 42
seed_everything(SEED)

train_loader_params = {'batch_size': 64,
'shuffle': True,
'num_workers': 0}

# Generators

training_set = FraudSequenceDataset(x_train, y_train,train_df['CUSTOMER_ID'].values, train_df['TX_DATETIME'].values,seq_len,padding_mode = "zeros")


Let us see how the first training batch looks like:

x_batch, y_batch = next(iter(training_generator))

x_batch.shape

torch.Size([64, 15, 5])

y_batch.shape

torch.Size([64])


The shape of x_batch is (batch_size= 64,number of features= 15, seq_len= 5) which is the expected input for a 1-D convolutional network or a recurrent model like an LSTM.

## 4.3. Convolutional neural network for fraud detection¶

Convolutional neural networks (CNN) are neural networks with specific convolutional layers that allow (1) detecting specific patterns or shapes in patches of input and (2) reducing spatial complexity when dealing with large inputs (e.g. an image with millions of pixels).

To do that, they replace the regular fully connected layer with a layer of convolutional filters that performs a convolution operation over the input neurons.

A convolutional layer has num_filters filters, with weights of a chosen dimension. If we consider a 2D-convolutional layer, each filter has 2 dimensions (width and height). In the example above, we consider an 8x6 input and a 3x3 filter. The convolution operation consists in sliding the filter over the input from left to right and from up to bottom, and each time computing the weighted sum of the input patch using the filter weights and applying an activation function to obtain an output similar to a regular neuron. In the figure, we represent with dashed squares on the left the first two input patches that the filter goes through and on the right the two corresponding outputs. Here we considered a stride parameter of 1, which means that the filter slides by 1 input each time. There is no padding, so the filter does not slide outside of the input, therefore the result is a map of features of dimension (8-(3-1))x(6-(3-1)), i.e. 6x4. But one can apply padding (considering that values outside the input are zeros) so that the output feature map has the same dimension as the input.

The convolution operation can be seen as a filter scanning the input to identify a specific pattern. The weights of the filters are learned by the model. Having multiple filters allows capturing multiple patterns that are useful to reduce the loss for the task at hand.

In order to summarize the information in final layers or to save memory in intermediate ones, the obtained feature maps can be aggregated into smaller maps with pooling layers (average, max).

### 4.3.1. 1D-convolutions¶

Note: Although not represented in the figure, if the input is an image, the input is in fact 3-dimensional (a 2D map of 3 features also called channels, namely RGB levels). The user only defines 2 dimensions for the filter (along the “sliding” directions) but the filters are in reality 3-dimensional as well, the last dimension matching the channel dimension.

2D-convolutions are only used to analyze inputs for which it makes sense to slide along 2 dimensions. In our case, to deal with transaction sequences, it only makes sense to slide along the sequence axis. Therefore, for fraud detection, we resort to 1D-convolutions and define a single filter dimension (with length equal to the number of consecutive sequence elements on which the filter looks for patterns).

### 4.3.2. Stacking convolutional layers¶

One can stack convolution layers just like fully connected layers. For instance, let us consider an input transaction sequence with 5 transactions and 15 features for each transaction. If one defines a convolutional neural network with a first 1D-convolutional layer with 100 filters of length 2 and a second convolutional layer with 50 filters of length 2. Without padding, the successive features map’s dimension will be the following:

• The input dimension is (5,15): 5 is the sequence length and 15 is the number of channels.

• The output dimension of the first convolutional layer is (4,100): each filter of dimension (2,15) will output a 1D feature map with 5-(2-1) = 4 features.

• The output dimension of the second convolutional layer is (3,50): each filter of dimension (2,100) will output a 1D feature map with 4-(2-1) = 3 features.

With padding, we can make sure that the sequence length does not change and obtain the dimensions (5,100) and (5,50) instead of (4,100) and (3,50).

### 4.3.3. Classification with a convolutional neural network¶

Convolutional layers produce high-level features that detect the presence of patterns or combinations of patterns within the input. These features can be considered as automatic feature aggregates and can then be used in a final regular fully connected layer for classification, after a flattening operation. This operation can be done using a pooling operator along the sequence dimension or with a flattening operator that simply concatenates the channels of all elements of the sequence into a single global vector.

### 4.3.4. Implementation¶

Convolutional layers are defined like regular layers. Instead of using the torch.nn.Linear module, one uses the specific layers for convolutional neural networks:

• torch.nn.Conv1d: this module defines a convolutional layer. The parameters are the number of input channels, the number of filters, and the dimension of the filters.

• torch.nn.ConstantPad1d: this module allows us to pad a sequence with a constant value (e.g. 0) to obtain the desired sequence length after the subsequent convolutional layer.

• torch.nn.MaxPool1d or AvgPool1d: these modules perform a pooling operation over the sequence dimension.

Let us define a FraudConvNet module that makes use of the above torch modules to take as input a sequence of seq_len transactions with len(input_features) features and predict if the last transaction is fraudulent. We will consider 2 padded convolutional layers, a max-pooling layer, a hidden fully connected layer, and an output fully connected layer with 1 output neuron.

class FraudConvNet(torch.nn.Module):

def __init__(self,
num_features,
seq_len,hidden_size = 100,
conv1_params = (100,2),
conv2_params = None,
max_pooling = True):

super(FraudConvNet, self).__init__()

# parameters
self.num_features = num_features
self.hidden_size = hidden_size

# representation learning part
self.conv1_num_filters  = conv1_params[0]
self.conv1_filter_size  = conv1_params[1]
self.conv1 = torch.nn.Conv1d(num_features, self.conv1_num_filters, self.conv1_filter_size)
self.representation_size = self.conv1_num_filters

self.conv2_params = conv2_params
if conv2_params:
self.conv2_num_filters  = conv2_params[0]
self.conv2_filter_size  = conv2_params[1]
self.conv2 = torch.nn.Conv1d(self.conv1_num_filters, self.conv2_num_filters, self.conv2_filter_size)
self.representation_size = self.conv2_num_filters

self.max_pooling = max_pooling
if max_pooling:
self.pooling = torch.nn.MaxPool1d(seq_len)
else:
self.representation_size = self.representation_size*seq_len

# feed forward part at the end
self.flatten = torch.nn.Flatten()

#representation to hidden
self.fc1 = torch.nn.Linear(self.representation_size, self.hidden_size)
self.relu = torch.nn.ReLU()

#hidden to output
self.fc2 = torch.nn.Linear(self.hidden_size, 1)
self.sigmoid = torch.nn.Sigmoid()

def forward(self, x):

if self.conv2_params:

if self.max_pooling:
representation = self.pooling(representation)

representation = self.flatten(representation)

hidden = self.fc1(representation)
relu = self.relu(hidden)

output = self.fc2(relu)
output = self.sigmoid(output)

return output


### 4.3.5. Training the 1D convolutional neural etwork¶

To train the CNN, let us reuse the same functions as in previous sections with the FraudSequenceDataset as Dataset and the FraudConvNet as module. The objective is the same as the feed-forward Network, so the criterion is binary cross-entropy as well.

seed_everything(SEED)

training_set = FraudSequenceDataset(x_train,
y_train,train_df['CUSTOMER_ID'].values,
train_df['TX_DATETIME'].values,
seq_len,

valid_set = FraudSequenceDataset(x_valid,
y_valid,
valid_df['CUSTOMER_ID'].values,
valid_df['TX_DATETIME'].values,
seq_len,

training_generator,valid_generator = prepare_generators(training_set, valid_set, batch_size=64)
cnn = FraudConvNet(x_train.shape[1], seq_len).to(DEVICE)
cnn

FraudConvNet(
(conv1): Conv1d(15, 100, kernel_size=(2,), stride=(1,))
(pooling): MaxPool1d(kernel_size=5, stride=5, padding=0, dilation=1, ceil_mode=False)
(flatten): Flatten(start_dim=1, end_dim=-1)
(fc1): Linear(in_features=100, out_features=100, bias=True)
(relu): ReLU()
(fc2): Linear(in_features=100, out_features=1, bias=True)
(sigmoid): Sigmoid()
)

optimizer = torch.optim.Adam(cnn.parameters(), lr = 0.0001)
criterion = torch.nn.BCELoss().to(DEVICE)
cnn,training_execution_time,train_losses_dropout,valid_losses_dropout = \
training_loop(cnn,
training_generator,
valid_generator,
optimizer,
criterion,
verbose=True)

Epoch 0: train loss: 0.11331961992113045
valid loss: 0.04290128982539385
New best score: 0.04290128982539385

Epoch 1: train loss: 0.046289062976879895
valid loss: 0.02960259317868272
New best score: 0.02960259317868272

Epoch 2: train loss: 0.036232019433828276
valid loss: 0.026388588221743704
New best score: 0.026388588221743704

Epoch 3: train loss: 0.032827449974294105
valid loss: 0.02484128231874825
New best score: 0.02484128231874825

Epoch 4: train loss: 0.030821404086174817
valid loss: 0.02410742957730233
New best score: 0.02410742957730233

Epoch 5: train loss: 0.029202812739931062
valid loss: 0.022835184337413498
New best score: 0.022835184337413498

Epoch 6: train loss: 0.028094736857421653
valid loss: 0.02244713509854825
New best score: 0.02244713509854825

Epoch 7: train loss: 0.027001507802537853
valid loss: 0.022176400977415873
New best score: 0.022176400977415873

Epoch 8: train loss: 0.026254476560235208
valid loss: 0.02218911660570509
1  iterations since best score.

Epoch 9: train loss: 0.02560854040577751
valid loss: 0.021949108853768252
New best score: 0.021949108853768252

Epoch 10: train loss: 0.024981534799554672
valid loss: 0.021592291154964863
New best score: 0.021592291154964863

Epoch 11: train loss: 0.024556038766430716
valid loss: 0.021545066185997892
New best score: 0.021545066185997892

Epoch 12: train loss: 0.024165517638524706
valid loss: 0.02139132608751171
New best score: 0.02139132608751171

Epoch 13: train loss: 0.02384240027745459
valid loss: 0.021200239446136308
New best score: 0.021200239446136308

Epoch 14: train loss: 0.023490439055142052
valid loss: 0.021294136833313018
1  iterations since best score.

Epoch 15: train loss: 0.02323751859669618
valid loss: 0.021165060306787286
New best score: 0.021165060306787286

Epoch 16: train loss: 0.02279423619230967
valid loss: 0.021365655278354434
1  iterations since best score.

Epoch 17: train loss: 0.022614443875238456
valid loss: 0.021119751067465692
New best score: 0.021119751067465692

Epoch 18: train loss: 0.022311994288852135
valid loss: 0.021248318076062478
1  iterations since best score.

Epoch 19: train loss: 0.02212963180260797
valid loss: 0.021625581627095863
2  iterations since best score.

Epoch 20: train loss: 0.02185415759852715
valid loss: 0.021411337640742094
3  iterations since best score.
Early stopping


### 4.3.6. Evaluation¶

To evaluate the model on the validation dataset, the command predictions_test = model(x_test) that we previously used on the Feed-forward Network won’t work here since the ConvNet expects the data to be in the form of sequences. The predictions need to be made properly using the validation generator. Let us implement the associated function and add it to the shared functions as well.

def get_all_predictions(model, generator):
model.eval()
all_preds = []
for x_batch, y_batch in generator:
# Forward pass
y_pred = model(x_batch)
# append to all preds
all_preds.append(y_pred.detach().cpu().numpy())
return np.vstack(all_preds)

valid_predictions = get_all_predictions(cnn, valid_generator)

predictions_df = valid_df
predictions_df['predictions'] = valid_predictions[:,0]

performance_assessment(predictions_df, top_k_list=[100])

AUC ROC Average precision Card Precision@100
0 0.852 0.569 0.261

Without any specific hyperparameter tuning, the performance seems to be competitive with the feed-forward neural network. A the end of this section, we’ll perform a grid search on this model for global comparison purposes.

## 4.4. Long Short-Term Memory network¶

As stated in the introduction, the transaction sequences can also be managed with a Long Short-Term Memory network (LSTM).

An LSTM is a special type of Recurrent Neural Network (RNN). The development of RNNs started early in the 80s [RHW86] to model data in the form of sequences (e.g. times series). The computations in an RNN are very similar to a regular feed-forward network, except that there are multiple input vectors in the form of sequence instead of a single input vector, and the RNN models the order of the vectors: it performs a succession of computations that follow the order of inputs in the sequence. In particular, it repeats a recurrent unit (a network with regular layers), from the first item to the last, that each time takes as input the output of hidden neurons (hidden state) from the previous step and the current item of the input sequence to produce a new output and a new hidden state.

The specificity of the LSTM is its advanced combination of the hidden state and the current sequence item to produce the new hidden state. In particular, it makes use of several gates (neurons with sigmoid activations) to cleverly select the right information to keep from the previous state and the right information to integrate from the current input. To get more into details with the specific mechanisms in the LSTM layer, we refer the reader to the following material: https://colah.github.io/posts/2015-08-Understanding-LSTMs/

### 4.4.1. LSTM for fraud detection¶

LSTM have been successfully used for fraud detection in the literature [JGZ+18]. The key information to remember is that when the LSTM takes as input a sequence of seq_len transactions, it produces a sequence of seq_len hidden states of dimension hidden_dim. The first hidden state will be based only on an initial state of the model and the first transaction of the sequence. The second hidden state will be based on the first hidden state and the second transaction of the sequence. Therefore, one can consider that the final hidden state is an aggregated representation of the whole sequence, as long as the sequence is not too long, which can be used as input in a feed-forward layer to classify the last transaction as either fraudulent or genuine.

### 4.4.2. Implementation¶

PyTorch provides a module torch.nn.LSTM that implements an LSTM unit. It takes as input the number of features of each vector in the sequence, the dimension of the hidden states, the number of layers, and other parameters like the percentage of dropout.

Let us use it to implement a module FraudLSTM that takes as input a sequence of transactions to predict the label of the last transaction. The first layer will be the LSTM module. Its last hidden state will be used in a fully connected network with a single hidden layer to finally predict the output neuron.

Note: Our DataLoader produces batches of dimension (batch_size, num_features, seq_len). When using the option batch_first = True, torch.nn.LSTM expects the first dimension to be the batch_size which is the case. However, it expects seq_len to come before num_features, so the second and third elements of the input must be transposed.

class FraudLSTM(torch.nn.Module):

def __init__(self,
num_features,
hidden_size = 100,
hidden_size_lstm = 100,
num_layers_lstm = 1,
dropout_lstm = 0):

super(FraudLSTM, self).__init__()
# parameters
self.num_features = num_features
self.hidden_size = hidden_size

# representation learning part
self.lstm = torch.nn.LSTM(self.num_features,
hidden_size_lstm,
num_layers_lstm,
batch_first = True,
dropout = dropout_lstm)

#representation to hidden
self.fc1 = torch.nn.Linear(hidden_size_lstm, self.hidden_size)
self.relu = torch.nn.ReLU()

#hidden to output
self.fc2 = torch.nn.Linear(self.hidden_size, 1)
self.sigmoid = torch.nn.Sigmoid()

def forward(self, x):

#transposing sequence length and number of features before applying the LSTM
representation = self.lstm(x.transpose(1,2))

#the second element of representation is a tuple with (final_hidden_states,final_cell_states)
#since the LSTM has 1 layer and is unidirectional, final_hidden_states has a single element
hidden = self.fc1(representation[1][0][0])
relu = self.relu(hidden)

output = self.fc2(relu)
output = self.sigmoid(output)

return output


### 4.4.3. Training the LSTM¶

To train the LSTM, let us apply the same methodology as the CNN.

seed_everything(SEED)

training_generator,valid_generator = prepare_generators(training_set, valid_set,batch_size=64)

lstm = FraudLSTM(x_train.shape[1]).to(DEVICE)
optimizer = torch.optim.Adam(lstm.parameters(), lr = 0.0001)
criterion = torch.nn.BCELoss()

lstm,training_execution_time,train_losses_dropout,valid_losses_dropout = \
training_loop(lstm,
training_generator,
valid_generator,
optimizer,
criterion,
verbose=True)

Epoch 0: train loss: 0.13990207505212068
valid loss: 0.02620907245264923
New best score: 0.02620907245264923

Epoch 1: train loss: 0.031434995676729166
valid loss: 0.023325502496630034
New best score: 0.023325502496630034

Epoch 2: train loss: 0.0276708636452029
valid loss: 0.021220496156802552
New best score: 0.021220496156802552

Epoch 3: train loss: 0.025479454260691644
valid loss: 0.020511755727541943
New best score: 0.020511755727541943

Epoch 4: train loss: 0.02423322853112743
valid loss: 0.019815496125009133
New best score: 0.019815496125009133

Epoch 5: train loss: 0.02332264187745305
valid loss: 0.019972599826020294
1  iterations since best score.

Epoch 6: train loss: 0.022942402170918506
valid loss: 0.01945732499630562
New best score: 0.01945732499630562

Epoch 7: train loss: 0.02235023797337005
valid loss: 0.0196135713384217
1  iterations since best score.

Epoch 8: train loss: 0.022119645514110563
valid loss: 0.019718042683986123
2  iterations since best score.

Epoch 9: train loss: 0.021690097280103984
valid loss: 0.01908009442885004
New best score: 0.01908009442885004

Epoch 10: train loss: 0.02134914275318434
valid loss: 0.01881876519583471
New best score: 0.01881876519583471

Epoch 11: train loss: 0.02092900848780522
valid loss: 0.019134794213030427
1  iterations since best score.

Epoch 12: train loss: 0.02074213598841039
valid loss: 0.019468843063232717
2  iterations since best score.

Epoch 13: train loss: 0.020374752355523114
valid loss: 0.01866684172651094
New best score: 0.01866684172651094

Epoch 14: train loss: 0.02008993904532359
valid loss: 0.01853460792039872
New best score: 0.01853460792039872

Epoch 15: train loss: 0.019746437735577785
valid loss: 0.01839204282675427
New best score: 0.01839204282675427

Epoch 16: train loss: 0.019563284586295023
valid loss: 0.01872969562482964
1  iterations since best score.

Epoch 17: train loss: 0.019375868797962006
valid loss: 0.01919776629834675
2  iterations since best score.

Epoch 18: train loss: 0.019136815694366434
valid loss: 0.017995675191311216
New best score: 0.017995675191311216

Epoch 19: train loss: 0.018896986096734573
valid loss: 0.01803255443196601
1  iterations since best score.

Epoch 20: train loss: 0.01882533677201451
valid loss: 0.018077422815306325
2  iterations since best score.

Epoch 21: train loss: 0.01855331002204834
valid loss: 0.018072079796384755
3  iterations since best score.
Early stopping


### 4.4.4. Evaluation¶

Evaluation is also the same as with the CNN.

valid_predictions = get_all_predictions(lstm, valid_generator)

predictions_df = valid_df
predictions_df['predictions'] = valid_predictions[:,0]

performance_assessment(predictions_df, top_k_list=[100])

AUC ROC Average precision Card Precision@100
0 0.857 0.658 0.279

This first result with the LSTM is very encouraging and highly competitive with the other architectures tested in the chapter.

It is worth noting that in this example, only the last hidden state is used in the final classification network. When dealing with long sequences and complex patterns, this state alone can become limited to integrate all the useful information for fraud detection. Moreover, it makes it difficult to identify the contribution of the different parts of the sequence to a specific prediction. To deal with these issues, one can use all the hidden states from the LSTM and resort to Attention to select and combine them.

## 4.5. Towards more advanced modeling with Attention¶

The Attention mechanism is one of the major recent breakthroughs in neural network architectures [BCB14]. It has led to significant advances in Natural Language Processing (NLP), for instance, in the Transformer architecture [VSP+17] and its multiple variants, e.g. into BERT [DCLT18] or GPT [RWC+19].

The Attention mechanism is an implementation of the concept of Attention, namely selectively focusing on a subset of relevant items (e.g. states) in deep neural networks. It was initially proposed as an additional layer to improve the classical encoder-decoder LSTM architecture for neural machine translation. It allows aligning the usage of the encoder hidden states and the element currently being generated by the decoder, and solving the long-range dependency problem of LSTMs.

The difference with the regular usage of an LSTM is that instead of only using the last hidden state, the Attention mechanism takes as input all the hidden states and combines them in a relevant manner with respect to a certain context. More precisely, in its most popular form, Attention performs the following operations:

• Given a context vector $$c$$ and the sequence of hidden states $$h_i$$, it computes an attention score $$a_i$$ for each hidden state, generally using a similarity measure like a dot product between $$c$$ and $$h_i$$.

• It normalizes all the attention scores with a softmax.

• It computes a global output state with a linear combination $$\sum a_i*h_i$$.

For applications like machine translation with an encoder-decoder architecture, the context vector will generally be the current hidden state of the decoder, and the Attention will be applied to all hidden states of the encoder. In such application, the encoder LSTM takes as input a sentence (sequence of words) in a language (e.g. French), and the decoder LSTM takes as input the beginning of the translated sentence in another language (e.g. English). Therefore, it makes sense to consider the current state of the translation as context to select/focus the right elements of the input sequence that will be taken into account to predict the next word of the translation.

### 4.5.1. Attention for fraud detection¶

For fraud detection, only an encoder LSTM is used in our implementation above. The choice of a relevant context vector will therefore be based on our intuition of what kind of context makes sense to select the right hidden states of the sequence. A reasonable choice is to consider a representation of the transaction that we aim at classifying (the last transaction) as context to select the right elements from the previous transactions. Two choices are possible: directly use the last hidden state as a context vector, or a projection of the last transaction (e.g. after applying a torch.nn.Linear layer). In the following, we resort to the second option, which is represented in the global architecture below:

Additionally to allow dynamic selection of the relevant hidden states for the sample at hand, the attention scores can provide interpretability by showing the parts of the sequence used for the current prediction.

### 4.5.2. Implementation¶

There is no native implementation of a simple Attention layer in the current Pytorch version (1.9). However, a more general torch.nn.MultiheadAttention, like the one used in the Transformer architecture, is available. Although it would allow implementing regular attention, we will instead make use of an unofficial module that implements a simpler attention mechanism for educational purposes. This Attention module is available in the following widely validated git repository: https://github.com/IBM/pytorch-seq2seq/blob/master/seq2seq/models/attention.py

Let us copy its content in the following cell:

# source : https://github.com/IBM/pytorch-seq2seq/blob/master/seq2seq/models/attention.py

import torch.nn.functional as F

class Attention(torch.nn.Module):
r"""
Applies an attention mechanism on the output features from the decoder.
.. math::
\begin{array}{ll}
x = context*output \\
attn = exp(x_i) / sum_j exp(x_j) \\
output = \tanh(w * (attn * context) + b * output)
\end{array}
Args:
dim(int): The number of expected features in the output
Inputs: output, context
- **output** (batch, output_len, dimensions): tensor containing the output features from the decoder.
- **context** (batch, input_len, dimensions): tensor containing features of the encoded input sequence.
Outputs: output, attn
- **output** (batch, output_len, dimensions): tensor containing the attended output features from the decoder.
- **attn** (batch, output_len, input_len): tensor containing attention weights.
Attributes:
linear_out (torch.nn.Linear): applies a linear transformation to the incoming data: :math:y = Ax + b.
mask (torch.Tensor, optional): applies a :math:-inf to the indices specified in the Tensor.
Examples::
>>> attention = seq2seq.models.Attention(256)
>>> context = Variable(torch.randn(5, 3, 256))
>>> output = Variable(torch.randn(5, 5, 256))
>>> output, attn = attention(output, context)
"""
def __init__(self, dim):
super(Attention, self).__init__()
self.linear_out = torch.nn.Linear(dim*2, dim)

"""
Args:
"""

def forward(self, output, context):
batch_size = output.size(0)
hidden_size = output.size(2)
input_size = context.size(1)
# (batch, out_len, dim) * (batch, in_len, dim) -> (batch, out_len, in_len)
attn = torch.bmm(output, context.transpose(1, 2))
attn = F.softmax(attn.view(-1, input_size), dim=1).view(batch_size, -1, input_size)

# (batch, out_len, in_len) * (batch, in_len, dim) -> (batch, out_len, dim)
mix = torch.bmm(attn, context)

# concat -> (batch, out_len, 2*dim)
combined = torch.cat((mix, output), dim=2)
# output -> (batch, out_len, dim)
output = F.tanh(self.linear_out(combined.view(-1, 2 * hidden_size))).view(batch_size, -1, hidden_size)

return output, attn


### 4.5.3. How does it work?¶

The custom Attention module above has a single initialization parameter which is the dimension of the input hidden states and the context vector. During the forward pass, it takes as input the sequence of hidden states and the context vector and outputs the combined state and the attention scores.

To familiarize with the module, let us manually compute the hidden states of our previous LSTM on a random training batch, and test the Attention mechanism.

x_batch, y_batch = next(iter(training_generator))

out_seq, (last_hidden,last_cell) = lstm.lstm(x_batch.transpose(1,2))


The outputs of the LSTM are the sequence of all hidden states, and the last hidden and cell states.

last_hidden.shape

torch.Size([1, 64, 100])

out_seq.shape

torch.Size([64, 5, 100])


Let us store the sequences of hidden states in a variable test_hidden_states_seq:

test_hidden_states_seq = out_seq


To create our context vector, let us apply a fully connected layer on the last element of the input sequence (which is the transaction that we aim at classifying) and store the result in a variable test_context_vector:

test_context_projector = torch.nn.Linear(x_batch.shape[1], out_seq.shape[2]).to(DEVICE)

test_context_vector = test_context_projector(x_batch[:,:,-1:].transpose(1,2))


The hidden states and context vectors of the whole batch have the following dimensions:

test_hidden_states_seq.shape

torch.Size([64, 5, 100])

test_context_vector.shape

torch.Size([64, 1, 100])


Now that the inputs for the Attention mechanism are ready, let us try the module:

seed_everything(SEED)
test_attention = Attention(100).to(DEVICE)

output_state, attn = test_attention(test_context_vector,test_hidden_states_seq)

output_state.shape

torch.Size([64, 1, 100])

attn[0,0]

tensor([0.2517, 0.2583, 0.2432, 0.1509, 0.0959], device='cuda:0',


We obtain two outputs. attn contains the attention scores of the hidden states and output_state is the output combined state, i.e. the linear combination of the hidden states based on the attention scores.

Here the components of attn are rather balanced because, since the module test_context_projector was only randomly initialized, the context vectors test_context_vector are “random” and not specifically more similar to a state than another.

Let us see what happens if the last hidden state test_hidden_states_seq[:,4:,:] is used as context vector instead.

output, attn = test_attention(test_hidden_states_seq[:,4:,:],test_hidden_states_seq)

attn[0,0]

tensor([7.5513e-09, 4.9199e-08, 1.3565e-06, 1.2371e-03, 9.9876e-01],


This time, it is clear that the attention score is much larger for the last transaction since it is equal to the context vector. Interestingly, one can observe that the scores decrease from the most recent previous transaction to the oldest.

Nevertheless, using the last hidden state as a context vector will not necessarily guarantee better behavior on fraud classification. Instead, let us hold on to our strategy with a feed-forward layer that will compute a context vector from the last transaction and train this layer, the LSTM and the final classifier (which takes as input the combined state to classify the transaction) altogether. To do so, we implement a custom module FraudLSTMWithAttention.

class FraudLSTMWithAttention(torch.nn.Module):

def __init__(self,
num_features,
hidden_size = 100,
hidden_size_lstm = 100,
num_layers_lstm = 1,
dropout_lstm = 0,
attention_out_dim = 100):

super(FraudLSTMWithAttention, self).__init__()
# parameters
self.num_features = num_features
self.hidden_size = hidden_size

# sequence representation
self.lstm = torch.nn.LSTM(self.num_features,
hidden_size_lstm,
num_layers_lstm,
batch_first = True,
dropout = dropout_lstm)

# layer that will project the last transaction of the sequence into a context vector
self.ff = torch.nn.Linear(self.num_features, hidden_size_lstm)

# attention layer
self.attention = Attention(attention_out_dim)

#representation to hidden
self.fc1 = torch.nn.Linear(hidden_size_lstm, self.hidden_size)
self.relu = torch.nn.ReLU()

#hidden to output
self.fc2 = torch.nn.Linear(self.hidden_size, 1)
self.sigmoid = torch.nn.Sigmoid()

def forward(self, x):

#computing the sequence of hidden states from the sequence of transactions
hidden_states, _ = self.lstm(x.transpose(1,2))

#computing the context vector from the last transaction
context_vector = self.ff(x[:,:,-1:].transpose(1,2))

combined_state, attn = self.attention(context_vector, hidden_states)

hidden = self.fc1(combined_state[:,0,:])
relu = self.relu(hidden)

output = self.fc2(relu)
output = self.sigmoid(output)

return output


### 4.5.4. Training the LSTM with Attention¶

The LSTM with Attention takes the same input as the regular LSTM so it can be trained and evaluated in the exact same manner.

seed_everything(SEED)

lstm_attn = FraudLSTMWithAttention(x_train.shape[1]).to(DEVICE)
lstm_attn

FraudLSTMWithAttention(
(lstm): LSTM(15, 100, batch_first=True)
(ff): Linear(in_features=15, out_features=100, bias=True)
(attention): Attention(
(linear_out): Linear(in_features=200, out_features=100, bias=True)
)
(fc1): Linear(in_features=100, out_features=100, bias=True)
(relu): ReLU()
(fc2): Linear(in_features=100, out_features=1, bias=True)
(sigmoid): Sigmoid()
)

training_generator,valid_generator = prepare_generators(training_set,valid_set,batch_size=64)

optimizer = torch.optim.Adam(lstm_attn.parameters(), lr = 0.00008)
criterion = torch.nn.BCELoss().to(DEVICE)

lstm_attn,training_execution_time,train_losses_dropout,valid_losses_dropout = \
training_loop(lstm_attn,
training_generator,
valid_generator,
optimizer,
criterion,
verbose=True)

Epoch 0: train loss: 0.10238753851141645
valid loss: 0.021834761867951094
New best score: 0.021834761867951094

Epoch 1: train loss: 0.026330269543929505
valid loss: 0.0203155988219189
New best score: 0.0203155988219189

Epoch 2: train loss: 0.024288250049589517
valid loss: 0.019749290624867535
New best score: 0.019749290624867535

Epoch 3: train loss: 0.02330792175176737
valid loss: 0.019204635951856324
New best score: 0.019204635951856324

Epoch 4: train loss: 0.02269919227573212
valid loss: 0.019130825754789423
New best score: 0.019130825754789423

Epoch 5: train loss: 0.022160232767046928
valid loss: 0.018929402052572434
New best score: 0.018929402052572434

Epoch 6: train loss: 0.02177732186309591
valid loss: 0.01847629409672723
New best score: 0.01847629409672723

Epoch 7: train loss: 0.02135627254457293
valid loss: 0.018755287848173798
1  iterations since best score.

Epoch 8: train loss: 0.02098137940145249
valid loss: 0.01887945729890543
2  iterations since best score.

Epoch 9: train loss: 0.020458271793019914
valid loss: 0.01891031490531979
3  iterations since best score.
Early stopping


### 4.5.5. Validation¶

valid_predictions = get_all_predictions(lstm_attn, valid_generator)

predictions_df = valid_df
predictions_df['predictions'] = valid_predictions[:,0]

performance_assessment(predictions_df, top_k_list=[100])

AUC ROC Average precision Card Precision@100
0 0.855 0.635 0.271

The results are competitive with the LSTM. Additionnaly, the advantage with this architecture is the interpretability of the attention scores for a given prediction.

In other settings, for instance with longer sequences, this model might also be able to reach better performance thant the regular LSTM.

## 4.6. Seq-2-Seq Autoencoders¶

The previous section of this chapter covered the use of regular autoencoders on single transactions for fraud detection. It is worth noting that the same principles could be applied here to create a semi-supervised method for sequential inputs. Instead of a feed-forward architecture, a sequence-to-sequence autoencoder with an encoder-decoder architecture could be used. This is not covered in this version of the section, but it will be proposed in the future.

## 4.8. Saving of results¶

Let us save the performance results and execution times of these three models in a Python pickle format.

performances_df_dictionary={
"CNN": performances_df_cnn,
"LSTM": performances_df_lstm,
"LSTM_Attention": performances_df_lstm_attn,
}

execution_times=[execution_time_cnn,execution_time_lstm,execution_time_lstm_attn]

filehandler = open('performances_model_selection_seq_model.pkl', 'wb')
pickle.dump((performances_df_dictionary, execution_times), filehandler)
filehandler.close()


## 4.9. Benchmark summary¶

Let us finally retrieve the performance results obtained in

and compare them with those obtained with CNN, LSTM, and LSTM with Attention.

The results can be retrieved by loading the performances_model_selection.pkl, performances_model_selection_nn.pkl and performances_model_selection_seq_model.pkl pickle files, and summarized with the get_summary_performances function.

# Load performance results for decision tree, logistic regression, random forest and XGBoost
filehandler = open('../Chapter_5_ModelValidationAndSelection/performances_model_selection.pkl', 'rb')

# Load performance results for feed-forward neural network
filehandler = open('performances_model_selection_nn.pkl', 'rb')

# Load performance results for CNN, LSTM and LSTM with Attention
filehandler = open('performances_model_selection_seq_model.pkl', 'rb')

performances_df_dt=performances_df_dictionary['Decision Tree']
summary_performances_dt=get_summary_performances(performances_df_dt, parameter_column_name="Parameters summary")

performances_df_lr=performances_df_dictionary['Logistic Regression']
summary_performances_lr=get_summary_performances(performances_df_lr, parameter_column_name="Parameters summary")

performances_df_rf=performances_df_dictionary['Random Forest']
summary_performances_rf=get_summary_performances(performances_df_rf, parameter_column_name="Parameters summary")

performances_df_xgboost=performances_df_dictionary['XGBoost']
summary_performances_xgboost=get_summary_performances(performances_df_xgboost, parameter_column_name="Parameters summary")

performances_df_nn=performances_df_dictionary_nn['Neural Network']
summary_performances_nn=get_summary_performances(performances_df_nn, parameter_column_name="Parameters summary")

performances_df_cnn=performances_df_dictionary_seq_model['CNN']
summary_performances_cnn=get_summary_performances(performances_df_cnn, parameter_column_name="Parameters summary")

performances_df_lstm=performances_df_dictionary_seq_model['LSTM']
summary_performances_lstm=get_summary_performances(performances_df_lstm, parameter_column_name="Parameters summary")

performances_df_lstm_attention=performances_df_dictionary_seq_model['LSTM_Attention']
summary_performances_lstm_attention=get_summary_performances(performances_df_lstm_attention, parameter_column_name="Parameters summary")

summary_test_performances = pd.concat([summary_performances_dt.iloc[2,:],
summary_performances_lr.iloc[2,:],
summary_performances_rf.iloc[2,:],
summary_performances_xgboost.iloc[2,:],
summary_performances_nn.iloc[2,:],
summary_performances_cnn.iloc[2,:],
summary_performances_lstm.iloc[2,:],
summary_performances_lstm_attention.iloc[2,:],
],axis=1)
summary_test_performances.columns=['Decision Tree', 'Logistic Regression', 'Random Forest', 'XGBoost',
'Neural Network', 'CNN', 'LSTM', 'LSTM with Attention']


The results are summarized in a summary_test_performances table. Rows provide the average performance results on the test sets in terms of AUC ROC, Average Precision and CP@100.

summary_test_performances

Decision Tree Logistic Regression Random Forest XGBoost Neural Network CNN LSTM LSTM with Attention
AUC ROC 0.797+/-0.01 0.868+/-0.02 0.87+/-0.02 0.869+/-0.01 0.876+/-0.01 0.872+/-0.01 0.876+/-0.02 0.874+/-0.02
Average precision 0.579+/-0.01 0.623+/-0.02 0.678+/-0.01 0.687+/-0.01 0.675+/-0.01 0.599+/-0.01 0.665+/-0.02 0.667+/-0.01
Card Precision@100 0.284+/-0.0 0.297+/-0.01 0.299+/-0.01 0.303+/-0.01 0.303+/-0.02 0.288+/-0.01 0.297+/-0.01 0.295+/-0.01

In the end, it appears that neural-network based approaches provide results that are competitive with random forests and XGBoost, providing slightly better performances in terms of AUC ROC, and slightly worse performances in terms of Average Precision. However, it should be kept in mind that, given more computational resources, a more extensive hyperparameter tuning could be performed for neural network-based approaches that would likely lead to further improve their performances.

## 4.10. Conclusion¶

In this section, automatic methods to build features from contextual data were explored. To classify a transaction as fraudulent or genuine, it is generally useful to resort to the regular behavior of the cardholder in order to detect a discrepancy. A manual method to integrate this contextual information is to proceed with feature engineering and the creation of expert feature aggregations.

Automatic representation learning methods from the Deep Learning literature have been explored to create features that represent the cardholder’s sequence of previous transactions in a way that optimizes the classification of the current transaction.

The key methodological components are the building of the data pipeline to automatically create the sequential data from landmark variables and the right combination of adapted neural networks such as the 1D-CNN, the LSTM, the Attention mechanism, etc. These models have been tested here, and they can provide competitive performance. In the future, other candidate architectures such as Multi-Head Self-Attention, Sequence-to-Sequence Autoencoder, or any combination of the above modules could be explored to attempt to further improve fraud detection performance.