Agent Spotlight: Sanjay Sharma, an Experienced And Multilingual Agent from India
March 9, 2023
March 24, 2023
5
min to Read
eCommerce
The payment industry has experienced significant transformation in recent years, particularly with the emergence of AI. With yearly chargeback losses from fraudulent disputes now costing merchants $125 billion worldwide, AI is set to optimize the chargeback dispute resolution process, reduce costs, and improve outcomes for businesses and customers alike.
Chargeback disputes occur when customers file a dispute with their bank or credit card issuer, challenging a transaction they believe to be fraudulent, unauthorized, or otherwise incorrect. This results in the reversal of the transaction and can lead to financial losses for businesses.
Resolving disputes internally can be time-consuming and labor-intensive. The process involves these steps:
This process is slow, and not doing it can be costly for businesses. If not resolved, chargebacks result in lost revenue made worse by chargeback fees.
AI is transforming the chargeback dispute resolution process by automating and streamlining various steps. Let's take a look at how AI is making this possible.
AI-powered systems, namely GPT-3, can automatically gather and analyze data related to a payment dispute. This includes transaction details, customer information, and relevant communication between the merchant and the customer.
By quickly analyzing this data, AI can help businesses identify the validity of a chargeback dispute and provide them with the necessary evidence to support their case.
AI can detect fraudulent transactions in real time by analyzing patterns and behaviors associated with fraudulent activities. Through identifying suspicious transactions before a chargeback dispute arises, businesses can proactively address potential issues and reduce the likelihood of disputes occurring.
AI's ability to streamline the chargeback dispute process results in a better experience for customers. Faster resolutions and more accurate decisions mean that customers are less likely to face lengthy disputes or to be unfairly penalized for legitimate transactions.
Invisible is a leader in both preparing data for powerful machine learning models and reinforcing the strength of models with a human-in-the-loop approach.
If an AI solution isn’t right for you, we perform chargeback dispute resolution at scale with trained operators with years of experience in recovering revenue for eCommerce companies.
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The payment industry has experienced significant transformation in recent years, particularly with the emergence of AI. With yearly chargeback losses from fraudulent disputes now costing merchants $125 billion worldwide, AI is set to optimize the chargeback dispute resolution process, reduce costs, and improve outcomes for businesses and customers alike.
Chargeback disputes occur when customers file a dispute with their bank or credit card issuer, challenging a transaction they believe to be fraudulent, unauthorized, or otherwise incorrect. This results in the reversal of the transaction and can lead to financial losses for businesses.
Resolving disputes internally can be time-consuming and labor-intensive. The process involves these steps:
This process is slow, and not doing it can be costly for businesses. If not resolved, chargebacks result in lost revenue made worse by chargeback fees.
AI is transforming the chargeback dispute resolution process by automating and streamlining various steps. Let's take a look at how AI is making this possible.
AI-powered systems, namely GPT-3, can automatically gather and analyze data related to a payment dispute. This includes transaction details, customer information, and relevant communication between the merchant and the customer.
By quickly analyzing this data, AI can help businesses identify the validity of a chargeback dispute and provide them with the necessary evidence to support their case.
AI can detect fraudulent transactions in real time by analyzing patterns and behaviors associated with fraudulent activities. Through identifying suspicious transactions before a chargeback dispute arises, businesses can proactively address potential issues and reduce the likelihood of disputes occurring.
AI's ability to streamline the chargeback dispute process results in a better experience for customers. Faster resolutions and more accurate decisions mean that customers are less likely to face lengthy disputes or to be unfairly penalized for legitimate transactions.
Invisible is a leader in both preparing data for powerful machine learning models and reinforcing the strength of models with a human-in-the-loop approach.
If an AI solution isn’t right for you, we perform chargeback dispute resolution at scale with trained operators with years of experience in recovering revenue for eCommerce companies.
LLM Task
Benchmark Dataset/Corpus
Common Metric
Dataset available at
Sentiment Analysis
SST-1/SST-2
Accuracy
https://huggingface
.co/datasets/sst2
Natural Language Inference / Recognizing Textual Entailment
Stanford Natural Language Inference Corpus (SNLI)
Accuracy
https://nlp.stanford.edu
projects/snli/
Named Entity Recognition
conll-2003
F1 Score
https://huggingface.co/
datasets/conll2003
Question Answering
SQuAD
F1 Score, Exact Match, ROUGE
https://rajpurkar.github.i
o/SQuAD-explorer/
Machine Translation
WMT
BLEU, METEOR
https://machinetranslate
.org/wmt
Text Summarization
CNN/Daily Mail Dataset
ROUGE
https://www.tensorflow
.org/datasets/catalog/
cnn_dailymail
Text Generation
WikiText
BLEU, ROUGE
Paraphrasing
MRPC
ROUGE, BLEU
https://www.microsoft.
com/en-us/download/details.a
spx?id=52398
Language Modelling
Penn Tree Bank
Perplexity
https://zenodo.org/recor
d/3910021#.ZB3qdHbP
23A
Bias Detection
StereoSet
Bias Score, Differential Performance
Table 1 - Example of some LLM tasks with common benchmark datasets and their respective metrics. Please note for many of these tasks, there are multiple benchmark datasets, some of which have not been mentioned here.
Metric
Usage
Pros
Cons
Accuracy
Measures the proportion of correct predictions made by the model compared to the total number of predictions.
Simple interpretability. Provides an overall measure of model performance.
Sensitive to dataset imbalances, which can make it not informative. Does not take into account false positives and false negatives.
Precision
Measures the proportion of true positives out of all positive predictions.
Useful when the cost of false positives is high. Measures the accuracy of positive predictions.
Does not take into account false negatives.Depends on other metrics to be informative (cannot be used alone).Sensitive to dataset imbalances.
Recall
Measures the proportion of true positives out of all actual positive instances.
Useful when the cost of false negatives is high.
Does not take into account false negatives.Depends on other metrics to be informative (cannot be used alone)and Sensitive to dataset imbalances.
F1 Score
Measures the harmonic mean of precision and recall.
Robust to imbalanced datasets.
Assumes equal importance of precision and recall.May not be suitable for multi-class classification problems with different class distributions.
Perplexity
Measures the model's uncertainty in predicting the next token (common in text generation tasks).
Interpretable as it provides a single value for model performance.
May not directly correlate with human judgment.
BLEU
Measures the similarity between machine-generated text and reference text.
Correlates well with human judgment.Easily interpretable for measuring translation quality.
Does not directly explain the performance on certain tasks (but correlates with human judgment).Lacks sensitivity to word order and semantic meaning.
ROUGE
Measures the similarity between machine-generated and human-generated text.
Has multiple variants to capture different aspects of similarity.
May not capture semantic similarity beyond n-grams or LCS.Limited to measuring surface-level overlap.
METEOR
Measures the similarity between machine-generated translations and reference translations.
Addresses some limitations of BLEU, such as recall and synonyms.
May have higher computational complexity compared to BLEU or ROUGE.Requires linguistic resources for matching, which may not be available for all languages.
Table 2 - Common LLM metrics, their usage as a measurement tool, and their pros and cons. Note that for some of these metrics there exist different versions. For example, some of the versions of ROUGE include ROUGE-N, ROUGE-L, and ROUGE-W. For context, ROUGE-N measures the overlap of sequences of n-length-words between the text reference and the model-generated text. ROUGE-L measures the overlap between the longest common subsequence of tokens in the reference text and generated text, regardless of order. ROUGE-W on the other hand, assigns weights (relative importances) to longer common sub-sequences of common tokens (similar to ROUGE-L but with added weights). A combination of the most relevant variants of a metric, like ROUGE is selected for comprehensive evaluation.
