They say the devil is in the detail, and inaccurate data hampers your business strategy and decisions. Poor quality data leads to inefficiencies, lost revenue, subpar risk assessments, and compliance issues.
One of the most effective ways to improve data quality is through data entry automation incorporating machine learning.
Why machine learning?
Because it is not enough to avoid data errors, and businesses must derive more strategic value via data analytics and insights. ML-based data entry automation does exactly that. It uses machine-learning algorithms to automatically identify and extract relevant data from structured and unstructured documents.
These algorithms process and make sense of large and complex datasets helping organizations gain competitive advantage while keeping a tab on market trends and consumer behavior.
Large-scale enterprises across healthcare, logistics, commercial real estate, lending, and legal can improve data accuracy, streamline document processing workflows, enhance compliance, shorten the turnaround time, and mitigate risks.
Let us delve deeper to understand the benefits of incorporating ML-based algorithms. We will also reveal the step-by-step process to automate data entry with machine learning.
So, let's jump right into it:-
Organizations can reap enormous benefits in the following areas by leveraging ML-based data entry automation.
Data entry automation tools incorporating ML algorithms help organizations analyze data for decision-making. Data is critical to business success because within these large and complex datasets lie the answer to emerging trends and investment opportunities in the market. Harnessing this data helps assess risks better and avoid overestimations. ML-based data entry tools capture and process large amounts of data significantly faster and more accurately.
Quality data is a critical component of predictive analytics. ML-based data entry improves data quality by reducing errors, filling in missing data points, and providing accurate datasets. It analyzes past data to provide accurate predictions and runs multiple what-if scenarios to design financial revenue models and improve growth.
ML-based tools enable faster and more efficient fraud detection. Banks are keen on detecting potential fraudsters based on their transaction data. For that, the bank needs to automatically extract data from various sources, such as bank statements, checks, and financial statements, and classify them. By processing the data and classifying transactions, they can use insights gained from the analysis to prevent possible fraud
According to an E&Y report, Risk management is the domain with the highest AI implementation rates (56%). ML algorithms are sought after because they can improve mitigation by analyzing large amounts of information and making predictions based on historical data. For example, it considers attributes such as credit history and loan repayment patterns, to predict the likelihood of default or late payments.
During an SEC (Securities and Exchange Commission) investigation in 2021, leading financial services company, JP Morgan failed to produce adequate written communication about business transactions and security matters.
The SEC penalized the firm with a whopping 125 million dollars for failing to implement compliance controls. Due to inadequate record-keeping, the employees could not access relevant information on time and could not comply with the investigations relating to a potential violation of federal security laws.
The cost of ineffective compliance is hefty, especially if there are inadequate records and data discrepancies relating to financial transactions. ML-based data entry creates a detailed audit trail documenting all data entry and processing activities. It extracts, classifies, and stores them in a centralized system you can access whenever needed. It further helps you demonstrate compliance with regulatory requirements and provide evidence in case of audits or investigations.
ML-based tools help reduce labor costs, improve accuracy, and free up your resources to focus on more strategic tasks. They are also more flexible. You can scale them as your data increases and becomes more complex.
Imagine a healthcare company that needs to record critical information such as patient demographics, diagnoses, lab tests, and medications. Extracting and processing this information from various documents is error-prone and time-consuming.
Let us examine how an ML-based document data entry tool automatically organizes and cleans raw data, transforms it into a machine-readable form, trains a model, and helps the healthcare firm generate real-time predictions.
Healthcare information such as claims data, medical imaging data, electronic health records, genomic data, etc., are sourced from disparate systems. And thus, they tend to be messy and complex. You can clean and transform data into a format easily recognized and processed by machine algorithms through preprocessing.
It ensures cohesion of entry types, making them suitable for a machine learning model while increasing the accuracy and efficiency of the model.
This technique involves removing or handling missing or erroneous data, such as duplicates, missing values, or outliers. For instance, in a diabetes diagnosis dataset of 1000 patients, you can impute the missing values for the BMIs of 20 patients with the mean or median of the corresponding feature.
It involves scaling data such as age and glucose levels. The former (age) has values ranging from 20 to 80, and the latter (glucose levels) ranges from 60 to 40. Through standard normalization techniques such as min-max scaling, z-score normalization, or log-transformations, you can scale the features to a range of 0 to 1.
In this stage, you assign predefined labels or categories to a data set. These labels are used to train an ML model to recognize patterns and make predictions based on new or unseen data. For example, you are assigning a label (i.e., "diabetic" or "non-diabetic") to each patient's data record in the dataset, with "1" typically indicating a diabetic patient and "0" indicating a non-diabetic patient.
It involves selecting, extracting, and transforming relevant features from the data to enhance the accuracy and performance of the model. For example, age, BMI, blood pressure, glucose, and cholesterol levels are the most suitable features for diabetes diagnosis.
This stage aims to select a machine learning algorithm that performs well against various parameters. In the context of diabetes diagnosis, you can train ML models on a labeled dataset containing features such as blood glucose levels, BMI, blood pressure, insulin levels, and diabetes pedigree function.
These models evaluate the relationship between these features and the expected outcome. Later, they are given a fresh set of unseen data, and the best-performing model is selected for further training.
Training involves using the algorithm to learn patterns or relationships in the data by adjusting its parameters until it achieves the best possible performance on a training set.
Ensemble methods combine multiple models to improve the overall performance. You can use techniques such as bagging, boosting, and stacking, including Random Forest, a popular ensemble learning method that combines multiple decision trees for accurate and stable prediction.
Deep learning algorithms typically involve multiple layers of neural networks, which enable the model to identify complex patterns and relationships in data. These models are used for various applications, including computer vision, natural language processing (NLP), speech recognition, and decision-making.
One example of deep learning in diabetes diagnosis is using convolutional neural networks (CNNs) to analyze retinal images and detect diabetic retinopathy (DR), a common complication of diabetes that can lead to vision loss.
Every algorithm requires input parameters from observed data. However, Hyperparameters are the parameters of a machine learning model that are not learned from the data during training but rather set before training.
These parameters control various aspects of the learning process, such as the complexity of the model, the learning rate, regularization, and so on.
The values of hyperparameters significantly impact the performance of a machine learning model, and hyperparameter tuning is the process of selecting the optimal values for these parameters.
Once you identify optimal hyperparameters, you can train the deep learning model on the entire dataset to obtain the final model. In deep learning, hyperparameters include settings such as learning rate, batch size, number of epochs, dropout rate, and regularization strength.
For a diabetes test, these settings are tuned for various ML-based models, such as a medical image for retinal imaging or blood glucose monitoring via EHR. Doing this can improve the model's accuracy in predicting patient outcomes and identifying potential health risks.
Grid search is a technique to tune the hyperparameters of a model by testing a range of parameter values and selecting the combination that yields the best performance. It is ideal for a small number of hyperparameters or when you know which hyperparameter values are likely to perform well.
On the other hand, random search samples hyperparameter values from a defined search space. It only covers some possible combinations of hyperparameters, but it can be more efficient when the search space is enormous.
This technique uses a probabilistic model to guide the search for optimal hyperparameters. In Bayesian optimization, we begin with a prior belief about the distribution of possible hyperparameters, often based on past experiments. It uses the results of previous evaluations to guide the search toward regions of the search space that are more likely to contain good hyperparameters. It is ideal for complex and high-dimensional search spaces like healthcare data sets.
Now that you have trained your model, assessing its precision is crucial. Model evaluation is the stage where you split the data into a training set and a test set for testing purposes.
Once you are done evaluating, only then can it be deployed into production. Deployment involves integrating the trained model into a larger system, such as a web or mobile application or your CRM.
Cross-validation is a powerful technique commonly used in healthcare. It involves training several machine learning models on subsets of the available input data and testing them on the complementary subset of the data.
It helps with evaluating the performance of a classification model. The confusion matrix is a table that shows the number of true positives, true negatives, false positives, and false negatives in the predictions made by a classification model. You can calculate various metrics such as accuracy, precision, and recall from these results.
This technique evaluates a model's sensitivity to changes in input parameters. In the context of diabetes diagnosis, it shows whether the model's predictions change when you vary the threshold for defining features such as high glucose levels, blood pressure, BMI, etc.
So far, we have explored the critical stages of ML-based data entry automation. These include:
Before implementing ML-based automated data entry, ensure you have found genuine problems and bottlenecks through a thorough workflow analysis. While well-trained ML algorithms are efficient, it is up to us to identify what data to enter, decide the format, and create rules.
As you implement ML-based data entry for your organization, remember that optimal ML algorithms rely heavily on the quality of your data and training, for this you can rely on data science course.
Without quality data, no machine learning model can work effectively. And hence, human supervision continues to stay crucial despite reduced manual efforts. With automated data entry, one should also ensure that information is always secured and your system complies with all relevant laws and regulations.
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