AI allows financial services providers to enhance customer experiences by automating and simplifying many processes, including document processing and onboarding, credit card fraud detection, insurance claims processing, investment analysis and more.
Artificial Intelligence can pose risks that could damage a company’s image if not managed appropriately.
Predictive analytics aid banks and financial services institutions in making data-driven decisions to enhance techniques of fraud detection, improve customer retention and enhance risk control. This is achieved by automating repetitive and resource-heavy processes while creating more personalized customer experiences.
AI can also reduce operational risks and costs by identifying anomalies in large volumes of sensitive information that would go undetected by humans, for instance detecting fraud by comparing data patterns of customers likely to default on loan payments with reliable customers.
AI can also help streamline business processes by rapidly and accurately evaluating and automating credit applications. AI can rapidly and accurately assess a borrower’s creditworthiness using both structured and unstructured data from various established sources – helping reduce risks such as overcharging low-risk customers or undercharging high-risk ones; additionally it can speed loan processing time significantly by using tools such as V7 Optical Character Recognition (OCR).
Machine learning (ML) algorithms can assist with risk management in financial services by eliminating human biases and errors, increasing data processing efficiency, producing higher-quality management reports and providing improved security/reliability of online payment processes (online credit card fraud losses are estimated to reach $48 billion per year by 2023, according to Insider Intelligence).
AI can assist companies in managing loan applications more efficiently by automating and speeding up the process, as well as by reducing investment risks by recognizing patterns and making real-time analysis-based decisions. AI enables firms to gain competitive edge while meeting industry regulations and anti-money laundering measures more easily.
Banks must recognize that Machine Learning (ML) may lead to unfair and biased decisions without the appropriate governance framework in place and without explanation of results. They must ensure they have appropriate data, regularly update models and use mathematic de-biasing as a form of mathematical correction to address algorithmic biases in models.
Natural Language Processing
Natural language processing translates unstructured data into something more usable, helping financial services firms quickly locate relevant information across a variety of unstructured resources such as emails and voice recordings to documents, news feeds, and textual data.
Large insurance firms use natural language processing (NLP) to sort through hundreds of reports and documents associated with one claim, automating part of the process while freeing up humans for more challenging work. NLP can also help virtual customer assistance systems that answer basic inquiries using human dialogue simulation; more complex queries will then be passed onto live agents for processing.
NLP can aid banks’ risk management by identifying and assessing anomalies such as patterns or trends in credit applications. This enables faster credit decisions as well as improving prediction power for existing or new risk models, helping banks identify risks earlier and mitigate them more effectively.
Robotic Process Automation
RPA automates rules-based processes and reduces human errors, speeding processing four times faster than human workers can manage. RPA is also reliable, scalable, and works without risk of absence or fatigue – qualities which make it ideal for 24/7 work environments.
RPA technology can streamline repetitive accounting and finance tasks such as data entry and invoice processing, while it can also take on more complex activities like assessing credit risks or analyzing insurance payouts.
RPA not only streamlines manual processes, but can also boost customer satisfaction and build resilience for your business. Consider, for instance, a bank that utilizes RPA to automate fraud detection process and frees employees up for more customer-service activities. Likewise, automated software updates more regularly than humans ensuring accuracy and consistency reducing manual workload such as reporting to management as well as increasing compliance officer efficiency by freeing them up for higher value activities.