Investment in AI is rapidly growing as financial services organizations continue to see the value it offers. See how Overbond’s COBI analytics contributes in two areas that are seeing transformations: Investment analysis and algorithmic trading.
Artificial Intelligence and augmented analytics may, for some, be the driving force behind our current technological revolutions. Perhaps even, the largest tech revolution we’ve yet to experience especially in the world of finance.
Financial services companies require detailed, accurate information to make informed decisions and offer valuable products to their customers. High-quality data helps ensure that financial decisions are made soundly, quickly, and with a reduced amount of risk.
Investment in AI is rapidly growing as financial services organizations continue to see the value it offers. At the enterprise level, several trends prevent organizations with new opportunities to disrupt traditional business models. Enhanced user engagement through recommendations, revenue growth with improved conversions, and cost optimization utilizing cognitive processing highlight just a few powerful ways AI is streamlining business and making organizations more intelligent.
There are a few key areas in which AI provides financial services companies with the ability to make powerful determinations for their organizations with greater efficiency and speed.
Determining whether to extend a line of credit to a business or individual is a critical financial service, requiring high-quality data to make informed decisions. Offering solutions to high-risk customers, making decisions about default risk using external data, or making judgments using incomplete information can place financial services companies and their users at an expensive risk.
AI makes it possible to paint a more complete picture of creditworthiness, allowing financial services companies to make better decisions. Using data such as transactional information and other behavioral data sets, newly developed classification models can give instant credit decisions with higher accuracy.
Over time, the self-improving model ultimately leads to better decisions whilst reducing the cost of access to customers. Even a 1% decrease in the default rate can improve the profitability of a loan portfolio whilst increasing the reach of financial products to long-tail financial consumers.
AI should be expected to continue assisting analytics tasks for financial services companies well into the future. Even in cases where machine learning is not robust enough to quickly and accurately make determinations, deep learning classifiers possess the ability to draw powerful insights from millions of different internal parameters.
Car Insurance Claims
Evaluating car claims is very demanding and expensive for insurers. Before a claim can be processed, highly skilled experts are often tasked with carrying out a visual analysis of the vehicle to prepare a summary report of potential claims.
Car insurers can use AI to streamline the claims process. In one instance, an insurer sought a system to use deep learning to estimate repair costs for car claims with greater accuracy.
Training dataset images that represent different types of car damages, including photos of cars in different lighting conditions, would eliminate common errors made during manual evaluations.
Working with NVIDIA, Ciklum developed a model to detect and define car parts using multi-label classification, using semantic segmentation to localize car damage and deliver a repair cost estimate. Through GPU acceleration using the NVIDIA P100 GPU instance, the speed of model training increased 1.67x faster with the same budget, reducing costs by up to 30% and eliminating the need to send inspectors to cars with minor damage.
Detailed data analysis helps B2B companies uncover new business opportunities. Because companies are often inundated with large volumes of leads and potential customers, a solution to separate the signal from the noise would contribute to nimble discovery, research, and follow-up.
In one case, streamlining the process required a data platform, consisting of a data access application and ranking algorithm. Together, the platform enables VPs to have instant access to consistent, validated data within days or even hours, enabling them to quickly identify opportunities, quantify potential, and follow through on changes.
One machine learning (ML) model automated the sourcing process, flagging only the opportunities that hold the highest potential. Another ML model could identify similar companies by the description of their business model without exact words. Using data from six million companies from multiple, heterogeneous sources simultaneously, the analytics platform also identifies significant changes across a variety of different companies and brings them to the attention of analysts.
The accelerated adoption of algorithmic trading helps financial service companies keep up with demands for rapid pricing and portfolio risk calculations. These demands, made possible through deep learning, require high-performance computing to execute calculations at a rapid clip.
Currently, there are models that enable GPU-assisted processing for improved latency. Augmenting apps written in C++ or Python without interference, calculations can be run in minutes instead of hours, improving speeds by up to 40x by placing GPUs in production. Faster processing for AI and ML allows traders to make decisions at the speed of business.
Artificial intelligence and augmented analytics continue to pave the way for companies looking to completely redefine how they work, innovate, protect and continually transform customer experiences. Adopting AI and augmented analytics allows financial service companies to streamline operations and make better decisions using comprehensive data. As the impact of financial services companies continues to rise, AI and augmented analytics capabilities should be expected to play a significant role in helping organizations boost their bottom lines for the foreseeable future.