With artificial intelligence (AI) becoming more widely accepted and assimilated into business processes, it’s no surprise to see processes in financial services fused with machine learning to enhance its features and strengthen its decision-making capabilities.
Simply put, machine learning is the application of AI, empowering systems with the ability to learn from experience without the need to be manually programmed. It does this by taking large volumes of data, observing how they interact, and identifying patterns, making it complete processes faster and improving decision-making. Machine learning helps programs identify areas that can be optimized and adjusts their algorithms accordingly to provide better results.
From social media, retail shopping, facial recognition on mobile phones, AI and machine learning are finding their way into our daily activities. Here’s a look at how it can change the game for financial services.
Applications of Machine Learning in Finance
Machine learning is known for its ability to analyze large volumes of historical data, analyzing behaviors, and identifying patterns to come up with data-driven decisions.
Better Fraud Detection
When it comes to identifying fraud and credit scoring, machine learning in finance can help automate these for better results. FP&A tools equipped with AI and machine learning algorithms can easily compare patterns of normal user behavior and identify unusual transactions that often point to fraud. These FP&A tools can then trigger notifications and alert account administrators without the charges being applied yet.
Personalization in Product and Service Offerings
Predictive analytics and machine learning can also help banks and financial services companies tailor their product offerings to customer profiles and preferences to have a much more personalized experience.
This can also be applied in customer service channels using AI chatbots that help customers navigate common questions and FAQs, update their accounts, and even pay their bills online.
Historical Data Analysis
Machine learning can be best applied in financial service institutions that work with historical data.
AI-powered financial tools can use their machine learning capabilities to gather insights from user behavior, identify performance trends, and help executives formulate conclusions and decisions backed by data.
Improving Security and Compliance
Compliance and security continue to be a considerable burden for financial organizations, especially today when cybersecurity breaches and business attacks are increasing.
Having to deal with large volumes of data can make any business susceptible to data integrity and privacy concerns.
Today, many financial service companies can now apply machine learning to audits, which reduces the need for manual intervention, cutting the risk for inaccuracies and helping managers step up further in terms of compliance.
Banks that use cloud environments to store their data online are susceptible to attacks that can cost millions in damages and personal data losses.
Banks and financial services must keep in mind that while pushing for digital transformation and adopting a more remote approach to data servers can help them become more agile; it can also increase their risk for malicious attacks.
These days, having a standard security framework is no longer enough to protect your network from the scale of today’s cyber threats.
Luckily, machine learning has been instrumental in analyzing user behavior and identifying unusual network activity, which goes a long way in putting a stop to fraud or security breaches when they do happen.
Getting Started With AI-Powered FP&A Using Performance Canvas
With financial services taking advantage of machine learning and predictive analytics, finance teams are better equipped to deal with large volumes of data and fast-track standard processes.
However, when it comes to harnessing the power of data and protecting the integrity of how these are shared and stored, investing in security is not a one-size-fits-all solution.
Financial firms should explore new FP&A features and solutions available today that can be tailored to adapt to their unique needs.
As an advanced FP&A tool, Performance Canvas gives its finance users an edge over its competitors. It is designed to make user adoption as seamless as possible while ensuring all data is stored securely, minimizing downtime. It also helps users see critical insights from their data quickly through historical versus actual comparisons, trend analysis, and behavior predictions among other things.
Find out why Performance Canvas is the easy choice for financial services companies today.
