The news is filled with predictions of an upcoming financial crisis.
Financial markets are like a rollercoaster, largely impacted by the global pandemic, which has resulted in soaring inflation, shutdowns, and a sluggish supply chain. Financial instability has increased with the higher energy and commodity process following the Russian invasion of Ukraine.  Â
Businesses must find ways to become more efficient. Intelligence software and analytics are now more important than ever to discover new revenue opportunities, limit risk, prevent fraud, reduce waste, and optimize the workforce.
Artificial Intelligence (AI)/Machine Learning (ML) models are being used daily at banks, insurance companies and other financial institutions. Despite the high level of adoption, however, many AI projects are not achieving their full potential. They are delayed or never make it to production because of the operational challenges of ingesting and processing vast amounts of data.Â
With all the rapid market shifts, fast data ingestion and analysis help financial institutions glean important insights to stay ahead of the curve.
Faster Data, Better ModelsÂ
The time to access and process data impacts every stage of the AI/ML model lifecycle. Here are just a few examples.
Data Discovery — The scope of an AI project is often limited by the data scientists’ ability to access the data needed to make accurate and meaningful predictions.Â
As more companies are shifting their AI workloads to the cloud, they are coming across greater data integration challenges. Some of the most common obstacles include reconciling disparate data that exists on different systems and merging data from diverse sources. This adds time and resources for data preparation, resulting in a project bottleneck.Â
When it is difficult to access live data often, data scientists are given a copy of the data. This presents a risk that the data will be outdated or biased, skewing the model’s results. This is especially risky when there are sudden and dramatic changes in the market, for example, supply and demand fluctuations that occurred after the Coronavirus outbreak.
If data scientists are given direct access to all the raw data with automated preparation, the process of ingesting and preparing data can be reduced significantly. This empowers data scientists while minimizing their dependence on other IT staff to have access to the data they need.Â
Model Building — Since the process of building models is experimental and requires multiple iterations, the ability to query large quantities of data on an ad-hoc basis is critical. The length of time it takes to complete a query can slow down development time. If a query can be performed quickly, more iterations can be completed in the same amount of time, and it is possible to include additional data, which will improve model accuracy.Â
Model Training — When processing times are excessive, data scientists are forced to reduce the size of their data sets, which can have an adverse impact on the model’s accuracy. If GPUs and other accelerators are used, processing time can be kept to a minimum, increasing the quantity of data that can be used as input. The more efficient the use of computing resources, the more data can be used, resulting in more accurate predictions.
Model Integration — As companies grow, there is more data to analyze and more parameters to consider. Many time-sensitive models will require the analysis of data in real-time with high-performance requirements to provide instant results. In addition, where there is a high data variability, it may be necessary to do real-time training.
The speed of data access and calculations becomes even more critical as the volume of data increases. If data is easy to access and data preparation is kept to a minimum, it is more likely that the model will stand the test of time and will scale in a cost-effective way.
See More: Top Five AI Frameworks to Consider in 2022
Fast Data in ActionÂ
Many companies are adopting a more efficient system architecture to reduce data preparation and processing times. The goal of these systems is to speed up data ingestion and processing times for more efficient resource utilization and quicker and more meaningful insights. The speed of reading and processing data can be directly related to a financial company’s competitiveness. Today, where more financial services, including loan approvals, payments, and cash transfers, are made online, having efficient and reliable online services is a must to stay competitive.
It is known that in times of financial instability, there is an increase in fraud. Now is the time to invest in more efficient and accurate fraud detection systems. A major global financial services provider using AI for a fraud detection system purchased an external data source and combined it with internal data to train a machine learning model. This provider selected an ML architecture based on GPUs for simultaneous processing and then selected a platform that loads data directly into a columnar database to speed up data feeds. With faster data access and processing, they were able to slash AI model training time from 3 ½ hours to 1 hour and 20 minutes.
A bank based in South Africa was limited to processing a credit history of 18 months because reading more historical data caused the system to crash. By using an architecture with a more efficient processing and faster data loading, they were able to read in a 10-year credit history, which enabled them to secure a major credit card company as a customer.
The current financial instability has increased the importance of machine learning models and analytics to minimize risk. AI/ML models are becoming an essential tool for financial organizations to create and maintain a competitive advantage. Tools that break through data siloes and give data scientists direct access to all the data can significantly speed up the data discovery process.Â
The ability to access huge volumes of data efficiently is essential to building accurate models that can deliver meaningful insights quickly. Where financial companies compete for insights, timing is everything, and fast data can make a huge difference.
What steps have you taken to enable faster preparation and processing of data in your organization? Let us know on FacebookOpens a new window , TwitterOpens a new window , and LinkedInOpens a new window .