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Financial Services and Insurance

Use AI solutions from Cleanlab to ensure high-quality data and models are used in key processes like: fraud detection, customer interaction, or risk assessment.
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Case StudyBanco Bilbao Vizcaya Argentaria (BBVA)

98%
reduction in required number of labeled training transactions
28%
improvement in ML model accuracy (with no change in existing modeling code)
Excerpt from an article by David M. Recuenco, Expert Data Scientist at BBVA:
[We used Cleanlab in] an update of one of the functionalities offered by the BBVA app: the categorization of financial transactions. These categories allow users to group their transactions to better control their income and expenses, and to understand the overall evolution of their finances. This service is available to all users in Spain, Mexico, Peru, Colombia, and Argentina.

We used AL [Active Learning] in combination with Cleanlab.

This was necessary because, although we had defined and unified annotation criteria for transactions, some could be linked to several subcategories depending on the annotator’s interpretation. To reduce the impact of having different subcategories for similar transactions, we used Cleanlab for discrepancy detection.

With the current model, we were able to improve accuracy by 28%, while reducing the number of labeled transactions required to train the model by more than 98%.

CleanLab assimilates input from annotators and corrects any discrepancies between similar samples.

CleanLab helped us reduce the uncertainty of noise in the tags. This process enabled us to train the model, update the training set, and optimize its performance. The goal was to reduce the number of labeled transactions and make the model more efficient, requiring less time and dedication. This allows data scientists to focus on tasks that generate greater value for customers and organizations.
BBVA is one of the largest financial institutions in the world. With a strong presence in multiple countries, BBVA offers a wide array of banking and financial services to individuals, businesses, and institutions.
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Graph showing results achieved with Cleanlab on a real dataset
Graph showing results achieved with Cleanlab on a real dataset

Case StudyCustomer Requests at Online Bank

To automatically triage future customer requests, intent classification is a standard Machine Learning task in customer service applications that requires well-labeled data. When run on a dataset of customer requests (text) annotated with 10 different intents, Cleanlab Studio found over 5% of dataset labels were incorrect and detected out-of-scope queries (outliers) like "how much is 1 share of aapl" and "is android better than iphone".

This dataset was also used for customer analytics, to determine the relative frequencies of different types of customer requests and which types are most common. However, some conclusions drawn from the original dataset are inaccurate due to the mislabeling and out-of-scope issues. Significantly more accurate conclusions were obtained by running the same analytics on the cleaned version of the dataset obtained from Cleanlab Studio.

Customer Requests at Online Bank

Using Cleanlab Studio to auto-fix label issues in this dataset led to a 16% improvement in prediction error without altering the existing LLM Transformer model or training code. Addressing additional data issues further enhanced accuracy without any model changes.

Businesses striving to make better decisions for customers must rely on accurate data-driven conclusions. These in turn rely on accurate data, which for this customer service application was easy to ensure with Cleanlab Studio.

HOW CLEANLAB IMPROVES KEY FINANCIAL PROCESSES

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Outlier & Anomaly Detection
Use advanced AI to detect outliers in text, images, and data files (Excel, CSV, JSON). Improve modeling of transactions, money reserves, and clients/providers. Cleanlab Studio offers comprehensive anomaly diagnosis beyond basic stats and past trends. Discover more with Cleanlab:
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Fraud Detection
Real-world training datasets often contain significant label noise, including overlooked cases of fraud. Cleanlab algorithms automatically detect and correct instances that are mislabeled, improving the reliability of fraud detection model training and evaluation.
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Risk Assessment
Cleanlab Studio enables data-centric AI to build accurate ML models for messy real-world tabular or text data. You can effortlessly harness AutoML for various data types, including text, image, and tabular formats (Excel, CSV, Json), allowing you to focus on the most important aspect: the data. Learn more:
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Aggregating Decisions/Predictions from Multiple Analysts
Cleanlab can analyze data annotated with multiple individuals’ decisions and estimate:
  1. consensus decision for each instance that aggregates the individual decisions.
  2. quality score for each consensus decision to gauge confidence that it is the correct choice.
  3. quality score for each annotator to quantify their overall skills.
Learn more and see benchmarks of the effectiveness of Cleanlab on real multi-annotator data.
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Data Auditing to ensure Compliance
Videos on using Cleanlab Studio to automatically detect errors in:


Cleanlab Studio auto-corrects raw data to ensure reliable analytics so you can make good financial decisions.

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