Artificial intelligence technology is gaining momentum in the financial sector, both among startups and established brick-and-mortar businesses. Deployments of AI in finance successfully assist process coordination between branches and departments, reinforce security measures, fight fraud, enhance risk mitigation, drive the development of new products and services, and help reduce operational costs.
By using AI-based solutions or transforming entire workflows around artificial intelligence, financial organizations can ensure they meet and surpass the demands of discerning customers.
One of the most important contributions of AI software development today is high-quality business process automation. The progress of machine learning in finance made it possible for software to improve with experience and move on from executing simple tasks to automating complex workflows that are typical of financial institutions.
Financial process orchestration is essential for any organization that aims to improve performance and capacity without inflating its workforce. An AI-based solution can be trained on internal, highly specific data, allowing small steps to be tailored and the end result to fit the exact needs of the business.
Artificial intelligence in finance can help bring previously disjointed tasks and processes together in a single, end-to-end financial platform for better visibility, tighter control, and smoother communication. Such systems can be scaled on demand without compromising accuracy or running into prohibitive operational costs.
Conventional credit scoring uses people’s credit histories to calculate the risk of underwriting a potential borrower. The process protects banks from irresponsible borrowers, but it can also hinder growth by denying loans to trustworthy individuals who don’t happen to have a credit history, e.g. young professionals.
AI-based credit scoring platform
This is where artificial intelligence comes in. AI-enabled credit scoring solutions employ predictive analytics and natural language processing to automate and scale the credit scoring process. Instead of credit history, they tap into available customer data — such as purchase history and social media activity — to assess how likely a person is to pay back loans in due time.
Assessment takes minutes instead of hours, enabling the bank to process more applicants, give credit access to more creditworthy clients, and raise profits while keeping the risks low.
We apply AI in finance to address a host of issues that financial organizations face, including fraud, performance bottlenecks, security gaps, system compatibility, usability, and more. Tap into our domain expertise and know-hows to stay ahead of the curve.
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AI in finance has proven an effective tool for insurance companies to enhance a whole range of tasks, from claims handling and underwriting decisions to customer service. Specialized insurtech solutions speed up claim classification and sorting, extract important data points, and build analytical models to detect anomalies that point to insurance fraud.
Besides fraud-scoring, machine learning is the basis of highly personalized insurance plan recommendations, providing clients with a quick and easy way to assess the merits of available options and keep premiums low.
As we mentioned above, AI is exceptionally good at processing text documents, far surpassing humans in speed and accuracy. Through continuous exposure to standardized documents such as loan forms, credit agreements, or contracts, AI solutions learn to effectively spot inconsistencies that could potentially lead to legal issues and create smart alerts about them.
With tasks like regulatory compliance out of the way, human employees are able to spend more time doing creative, higher-value work and exploit new business opportunities.
For many major banks and financial institutions, digital transformation challenges start with a heap of legacy documentation stored on paper. Valuable historical data that could help train machine learning algorithms is rendered useless and massive effort is required to make physical documents readable for machines.
Thankfully, there are now computer vision solutions such as Parascript or Leverton that are able to accelerate and enhance this process, extracting data and organizing legacy paperwork hundreds of times faster than humans. Eventually, bank employees get access to smart search through digitized documents using content-aware filters.
Automated document classification and data entry
Using machine learning and natural language processing in digitization results in huge time and cost savings, better transparency, and eliminates human error where the cost of a misread number is prohibitively high.
For investors who stay conscious about social, environmental, and governance issues while building their investment portfolio, a company’s ESG score can be decisive. The score reflects how well an organization fares in such areas as climate change, board diversity, tax transparency, ethical sourcing, and more.
ESG scoring process
AI scoring solutions provide unparalleled speed and repeatability of data analysis, enabling real-time monitoring of company activity across multiple sources. Supported by human-led research, AI-generated ESG scores help businesses and individuals more accurately assess the sustainability of their investment opportunities, improve long-term strategy with hard data, and connect with companies whose values match their own.
In recent years, the role of AI in trading has been expanding significantly, now reaching up to 80% in the stock market. Like human traders, machines can learn to discover meaningful patterns by examining historical data. Combined with current information from around the web, this allows algorithmic trading systems to make accurate predictions about the behavior of stocks, bonds, commodity, and currency prices in changing market conditions.
As trading is heavily dependent on emotion, many automated trading solutions now employ sentiment analysis to process call transcripts, news stories, and social media posts, and turn unstructured data into actionable insights. Machines also outperform humans in spotting anomalies in real time, enabling users to profit from fast-evolving opportunities that might be missed by the human eye.
With the advent of online payments and mobile banking, financial companies find themselves exposed to new security challenges. To successfully withstand malicious attacks 24/7, fintech systems have to stay up to date with cutting-edge technologies like artificial intelligence.
Biometrics solutions are popular with financial organizations that are looking for next-gen data and asset protections. The range of biometric deployments includes computer vision, voice biometrics, keystroke recognition, fingerprint recognition, and more.
While biometric systems are incredibly easy to use, they’re cheaper to support and harder to trick than password- or token-based authentication systems. Underpinned by lightning-fast AI analysis, these techs streamline authentication and authorization for company employees as well as clients across a wide range of devices.
High-performance solutions like DataVisor on the basis of unsupervised machine learning have evolved into powerful tools for addressing fraud and financial crimes. Trained on millions of past transactions and publicly available data, an AI solution is able to spot and flag suspicious spending behaviors early, helping to prevent credit card fraud while eliminating false positives.
AI-powered pattern detection
AI in finance is also adept at financial risk assessment that informs strategy and investment policies. Companies apply AI-driven predictive analytics to generate detailed market reports, research interest rates, and forecast future sales.
While online threats tend to be in the spotlight with huge sums of money on the line, the bank’s ATMs are also a source of many concerns and vulnerabilities. Using AI and IoT tandem, banks can incorporate and scale predictive maintenance software across the entire ATM network. The solution tracks performance of every machine and issues alerts to maintenance staff before an ATM breaks down. Inactive ATMs lose a bank its service fees and create customer frustration, so keeping them operable 24/7 is in every bank’s best interest.
However, a perfectly functional ATM can still be a sitting duck for criminals. This is why one of the latest trends in banking is AI-enabled face recognition software. Installed into an ATM, face recognition ensures reliable identity verification of card holders and makes it virtually impossible to withdraw funds using someone’s else’s card.
With artificial intelligence in finance, service providers are able to learn more about their customers’ habits and preferences than ever before, and use that knowledge to personalize experiences.
A personal touch is very important for customer satisfaction, but until recently it was not possible for digital assistants to imitate human conversation convincingly. Advances of artificial intelligence in finance have enabled modern chatbots to understand complex questions, precisely categorize tickets, and even proactively address issues without human supervision.
Conversational banking software allows stepping up regular customer service through the use of NLP and AI to handle entire conversations with clients. This approach has a number of benefits.
First of all, letting conversational AI take care of repetitive and time-consuming tasks frees up valuable human resources for more sophisticated work and reduces service costs down the line. Faster response times and 24/7 availability improve customer satisfaction and create positive word-of-mouth among clients.
In addition, a user-friendly conversational interface helps users discover banking products and services through preferred digital channels and at their own pace. With seamless onboarding and proactive follow-ups on incomplete transactions, AI-powered banking assistants amplify conversion and keep drop-offs to a minimum.
The ability of artificial intelligence to build comprehensive, up-to-date consumer profiles by sifting through unstructured personal data makes this technology a great fit for personal financial management solutions. AI-driven PFM apps like Mint adapt easily to mobile use, turning smartphones into customized financial advisors that you can carry in your pocket and consult on-the-go.
Beyond guiding users to more organized and money-saving financial habits, these programs cover entire financial journeys, simplifying processes like credit scoring, account opening, and loan processing.
For high-profile bank clients, the data-crunching and behavior mapping capabilities of AI in finance are crucial for automating wealth management. For decades, curating a personal investment portfolio for a wealthy client has involved many hours of scrupulous research, compliance worries, and skyrocketing fees. Thanks to machine learning in finance, it is now possible to generate fact-based, fully compliant investment recommendations in a matter of minutes, at a fraction of the cost.
Readily available financial advice helps bank customers arrive at decisions quicker and with more confidence, while the banks boost customer loyalty and streamline managing a client base with different risk appetites.
The current proliferation of fintech tools that are as powerful as they are accessible is largely the result of smartphone adoption combined with cheaper, mobile-optimized artificial intelligence solutions.
Major fintech players and startups are now able to leverage features like geofencing, data encryption, face recognition, secure cloud storage and processing, and more. Smartphones also opened access to personal data such as device-generated media, location, and browsing history that is invaluable for customer behavior analysis.
As one of the most data-rich domains, the finance industry stands to benefit from disruptive artificial intelligence technology that uses these data troves to become more sophisticated. Due to immediate benefits, AI applications in finance receive well-deserved attention from the firms who want to invest in long-term strategy.
It must be noted, however, that not all sectors within the industry are equally suited to full AI-enabled automation. In some cases (trading, ESG scoring, wealth management) the findings made by AI models need to be assessed or enhanced with human involvement to provide optimal results.