AI implementation is a common practice for financial organizations looking for predictive analytics to enhance their decision-making and minimize business risks. However, the integrity of finance data used to train the AI/ML models plays an important role in ensuring the reliability of its outcomes. This is because AI algorithms need an immense amount of data to learn, evolve, and perform the desired actions. Any discrepancies in the input data result in flawed insights, inaccurate financial forecasting, and misguided business decisions.
In the worst-case scenarios, the entire AI/ML model might go down into flames if the training data is of poor quality. Thus, data cleansing is an important step in implementing AI-driven models and processes and ensuring their success. Here’s a 5-step data cleansing checklist to prepare finance data for AI to ensure that your organization gets the most out of AI-driven financial insights:
Step 1: Data Profiling
Data profiling is the first step in any comprehensive data cleansing exercise that helps in understanding the current state of the data. Here, outliers, anomalies, inconsistencies, incomplete fields, and errors that may affect downstream AI processes are identified. And given the complex nature of financial data, profiling becomes important. Missing this step leads to unreliable outputs as AI models are fed with inaccurate or incomplete data.
Suppose you’ve 100 invoices in a dataset where 95 of the invoices are in thousands and 5 in millions of dollars. Needless to say, analyzing them together would lead to inaccurate results. Data profiling helps in identifying such outliers to either eliminate them or transform using techniques like log transformation or winsorization. Professional data cleansing service providers usually leverage z-score, a simple statistical metric used to spot outliers in financial data.
In a nutshell, data profiling serves as a roadmap for future steps of the data cleansing process by identifying areas requiring the most attention, such as missing values or duplicated records, and creating a clear strategy for addressing these issues.
Step 2: Eliminating Duplicates and Inconsistencies
Financial data is vast and varied. For example, transactional data can be present in the form of dollars, euros, rupees, dirhams, and more. Such inconsistencies often arise from factors like input errors or different data formats. If left unattended, these inconsistencies skew financial analyses and mislead AI models as these rely on patterns within the data.
Moreover, unverified duplicate records may lead to erroneous insights or misleading trends. A duplicate customer transaction entry, for instance, may lead AI algorithms to overstate revenue, potentially impacting financial forecasting models.
Investing in tailored data cleansing solutions helps financial institutions to automate much of this task, providing a faster and more accurate resolution than manual efforts. Moreover, having automated solutions to remove inconsistencies and duplicate entries ensures the integrity of financial data and enhances the reliability of AI-generated insights.
Step 3: Handling Missing Data
As mentioned already, AI models need complete datasets to make accurate predictions. On the other hand, gaps in financial datasets drastically impact AI models by limiting their efficiency. Whether due to incomplete records, human error, or system limitations- whatever the reason might be, missing data entries should be addressed during the cleansing process.
There are multiple approaches to handle incomplete data. Imputation techniques, such as using averages or medians to fill in gaps, can be employed when data loss is predictable and small. Machine learning techniques help in inferring missing values in more complex cases based on existing patterns in the datasets. Professional data cleansing companies leverage advanced tools and technologies to handle missing data efficiently and ensure that the gaps in the financial data do not hinder your AI initiatives.
Nevertheless, the choice of method should be determined by the impact that missing data might have on specific financial processes. Imputation, for instance, might be effective for less sensitive financial variables but is inappropriate for high-risk data, such as credit ratings or loan defaults. Thus, a strategic approach is required to mitigate the risks posed by incomplete datasets.
Step 4: Data Normalization
As the name suggests, normalization includes putting data into a standard format, since most of it comes from various sources like customer databases, third-party vendors, accounting systems, etc. As each source has a different format, data normalization becomes important here. Inaccurate or unstandardized data negatively impacts the efficiency of AI algorithms, as mismatches between data types and formats can result in unreliable predictions.
For AI models to work effectively, the data must be structured uniformly based on a set of predefined rules. This helps in reducing redundancies and ensuring that the information is accurately mapped and categorized, regardless of the data source. In short, data normalization improves the overall usability of financial data by ensuring that all the fields are properly aligned.
Step 5: Validation and Quality Assurance
No matter how meticulous your data cleansing efforts are, errors might still occur, especially in large financial datasets. Thus, validating the data before deploying it in AI systems is the last and most important phase of the five-step data cleansing checklist. Here, cleansed data is compared against the original datasets and external benchmarks to ensure its accuracy.
Additionally, practicing quality assurance periodically helps in reviewing the data for potential issues that might arise even after thorough cleansing. AI applications in finance, like credit scoring and fraud detection, require continuous monitoring to ensure that the underpinning data remains accurate and relevant all throughout.
Quality assurance also includes ongoing monitoring post-deployment to ensure that future data inputs also adhere to the same quality standards. Implementing an automated system for continuous data validation helps prevent data degradation and maintains the integrity of your AI-driven financial models.
Closing Lines
As finance functions increasingly adopt AI, the performance of these algorithms depends upon the quality of the training data used. Inaccurate and erroneous data skews the results and drives poor decision-making. In contrast, clean and accurate data helps in harnessing the full potential of AI for financial analysis, decision-making, and forecasting.
Following the above-mentioned 5-step data cleansing checklist ensures that your financial data is accurate, consistent, and reliable- empowering AI to deliver reliable and actionable insights. Moreover, optimized AI initiatives lead to more accurate financial reporting, better compliance, and offer businesses an upper hand in cutting through the competition in today’s fast-paced financial landscape.
The post Preparing Finance Data for AI: A 5-Step Data Cleansing Checklist appeared first on Datafloq.
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