The moment for cloud technology in the financial sector is now
Due to a widespread transition to the cloud, the number of cloud providers has grown exponentially with the market now consisting of “more than 360 vendors across 21 market segments, delivering more than 550 PaaS offerings” according to Gartner. An expanding market offers suitable products even for organizations with complex needs such as financial entities.
However, with so many options to analyze, finding the right cloud solution to suit a specific use that meets both business and customer needs is challenging. From custom systems to the integration of multiple market-ready tools, there is such a wide range of solutions financial institutions can choose from. Lengthy and costly research and experimentation is necessary if companies are to identify the most efficient cloud integration. Successful cloud usage relies on in-depth knowledge of the ever-changing market and expert skills that not all companies within the financial sector have in-house.
As the transfer and monitoring of developments in cloud technologies is so demanding, organizations that could benefit from this digital transformation are at risk of being left behind.
So, how can financial institutions manage this complex process?
How AI Labs simplifies and accelerates cloud integration
The goal of AI Labs is to streamline the integration process of cloud technologies for financial entities by conducting rapid research and experimentation on their behalf. Expert knowledge is used to define the best combination of cloud computing technologies for different uses.
With an understanding of the concerns around the use of cloud technologies in the financial sector (such as privacy, data protection and changing regulations) alongside a focus on specific use-cases, AI Labs provides independent and detailed guidance on navigating the ever-growing cloud technologies market. Validated cloud solutions are productionized and scaled, enabling financial entities to integrate suitable solutions with minimum effort and resources.
Therefore, the financial industry can leverage the state-of-the-art services made available by cloud providers beyond what they are capable of individually. To demonstrate the value of such guidance, let’s discuss two specific situations where AI Labs aided financial entities in finding the right cloud tool.
Use Case 1: Identifying a system for document data extraction
In the financial sector, data is collected from a variety of sources and models are trained to be able to process each and every one. Documents present their own data extraction challenge. How should a financial organization collect data from checks, invoices, payslips, receipts and similar documents?
When deciding on a system for document data extraction, companies are faced with two main options. Either they integrate a market-ready system from a cloud provider, or they invest in a custom model that they train in-house. The decision depends on whether a model trained by a third-party provider will be sufficient to extract key-value pairs from documents. Alternatively, would investment in a custom document recognition model be more efficient?
Choosing the right solution is essential considering the nature of the data in question. Financial entities process sensitive and private documents. Therefore, accuracy and Data Loss Prevention capabilities are key factors in whether a solution is fit for purpose.
Through AI Labs' research and experimentation, guidance was developed to help financial entities make this decision based on the specifics of their situation. Questions establishing complexity and technical considerations are included in the guidance, bringing organizations to the right conclusion for them. For example, a determining factor in this scenario is the nature of the documents. It’s easier to train models to extract data from structured documents compared to unstructured documents.
The guidance produced by AI Labs has reduced the decision-making process for financial entities, allowing them to swiftly move on to integrating a suitable data extraction system.
Use Case 2: Finding the most effective solution for large datasets
There are various market-ready cloud solutions for the classification and forecasting of large datasets with a lot of features. A popular option for banks is the product Spark MLlib. Is there a more effective alternative for dealing with large datasets containing over one million time-series?
AI Labs researched and experimented with solutions on behalf of financial entities to provide guidance and information on suitable alternatives that offer an improved experience. For binary classification and time series forecasting to take place successfully, a Recurrent Neural Network is trained to process selected instances of datasets efficiently.
A key consideration for this cloud tool is that large datasets require advanced capabilities to process. So, while analyzing the options from cloud providers, features such as parallel computing (horizontalization) and increasing a single machine capacity (verticalization) are highly regarded.
As in the case above, AI Labs outlined the cloud tools that are best suited for different scenarios, taking into account the complexity and technical considerations of the data processing in question. Consequently, companies can easily identify the cloud product to add to their portfolio.
Leveraging cloud solutions is a requirement of financial entities as digital transformation continues to progress at a rapid speed. As there is such a range of cloud providers offering a variety of options as well as custom creation available, deciding on a particular solution that is the best, is nearly impossible.
AI Labs takes on the challenge of a mobile market and fulfills the role of researcher, tester and expert for financial entities embracing cloud integration. It enables them to do so swiftly, with minimum effort and avoiding unnecessary costs. The two cases discussed demonstrate how AI Labs bridges the gap between user and provider for improved results.