- Key drivers of the aggressive digital transformation in financial service companies
- How do banks use AI?
- AI as risk management game-changer
- Pros and Cons of AI in banking risk management
- What are the challenges to AI risk management in banking?
The ability of machine learning models to analyze large amounts of data - both structured and unstructured - can improve analytical capabilities in risk management and compliance, allowing risk managers in financial institutions to identify risks in an effective and timely manner, make more informed decisions, and make banking less risky.
Countless AI applications in the financial services ecosystem can identify patterns and connections that humans can't, thus giving possibilities to improve and augment financial business processes.
This is why, banks are investing in new technologies and processes, such as machine learning and Artificial Intelligence, digital identity systems, and blockchain.
Key drivers of the aggressive digital transformation in financial service companies
- Relentless competition between traditional banks and agile fintech and digital-only banks that attract customers with state-of-the-art service.
- The new focus on automation, Big Data, analytics, and innovation adopted in many sectors of the economy including the finance requires an agile architecture to support the digital ecosystem.
- Customers are getting more digital and tech-savvy, customers of new generations, especially under 35 want to be online and use all benefits of digital services.
- End-to-end digital transformation of banks is necessary to leverage the latest technologies and innovative approaches and optimize operative and cost efficiencies.
The disruption of the financial services industry has just started and AI and blockchain are the technologies that will be on the spot. According to Synechron, AI development will move away from simple automation and focus on cognitive use cases across sales, trading, wealth management and compliance sectors of the financial services industry.
Synechron also predicts that data will get “bigger” as big data initiatives push more intelligent and more open business models and better data tools and visualizations. Data virtualization, data lineage, and data visualization will also become increasingly important to gain additional value and intelligence from data.
How do banks use AI?
Today most banks and credit unions are only starting using AI. Nevertheless, the survey conducted by Narrative Science and the National Business Research Institute showed that 32% of financial services executives surveyed use AI technologies such as predictive analytics, recommendation engines, voice recognition, and response.
The stats by Statista show that the top banking areas using AI are cards and payments. The analysis of America’s 7 top banks explores the applications of AI in seven leading commercial banks in the USA as ranked by the Federal Reserve. According to this analysis, AI is used by the banks in complex, repetitive processes and data analysis.
For example, JPMorgan Chase has invested in technology and recently introduced a Contract Intelligence (COiN) platform designed to “analyze legal documents and extract important data points and clauses”. This machine learning technology allows analyzing thousands of commercial agreements in seconds and this means a dramatic reduction of the time spent on back-end processes.
The most visible form of AI being adopted across the sector for the moment is the chatbots actively used in the front office of financial institutions. Bank of America adopted AI technology for its intelligent virtual assistant, which uses “predictive analytics and cognitive messaging” to provide financial guidance to the company’s over 45 million customers.
Another application of AI in banking is fraud identification and eradication. For example, CitiBank partners with tech companies to improve their services and stay in the forefront. One of its strategic investments is Feedzai, a leading global data science enterprise that uses “machine-based learning” to evaluate “big data” and potentially fraudulent activities in all avenues of commerce including online and in-person banking.
However probably the most important application of AI in the financial services sector is risk management as the estimates show that on an average loss of merchants due to fraud attacks is 1.5% of their annual revenue.
AI as risk management game-changer
Artificial Intelligence is a game-changer for risk management in finance as it provides banks and credit unions with tools and AI solutions to identify potential risks and fraud.
The financial crisis of the previous decade gave financial services firms a lot of problems with credit-challenged consumers. Before the digital revolution in financial services industry customer intelligence was based on some relatively simple heuristics, the customer value data was gained through focus groups and surveys of consumer behavior the results of which didn’t always correspond the reality.
Today new technologies give businesses access to really tremendous amounts of data about consumers’ behavior and needs.
Another great source of problems for banks is online lending technology and the emergence of alternative lenders. Capgemini insights reveal that “these non-traditional lenders use technology-based algorithms and software integrations to assess credit profiles of customers and are also leveraging alternative data such as social media photos and check-ins, GPS data, e-commerce, and online purchases, mobile data, and bill payments.”
Risk management in banks should use cognitive technologies to gain competitive advantage and use risk to power their organizations’ performance.
Artificial intelligence and risk management perfectly align when there is a need for handling and evaluating unstructured data. It is estimated that risk managers of financial institutions will focus on analytics and stopping losses in a proactive manner based on AI findings, rather than spending time in managing the risks inherent in the operational processes.
AI solutions are able to fuel financial institutions with trusted and timely data for building competence around their customer intelligence and successful implementation of their strategies.
Pros and Cons of AI in banking risk management
Given the development of digital technologies and decreases in the cost of data storage, Artificial intelligence is becoming an integral part of business processes. Machine learning allows handling and analyzing unstructured data, thus saving time and money for financial services companies.
AI in banking risk management can lower operational, regulatory and compliance costs and provide reliable credit scorings for credit decision-makers. Risk assessment AI can provide a fast and accurate risk assessment, using every data - both financial and non-financial - it can find to factor in the character and capacity of a customer.
AI-powered risk management solutions can be also used for model risk management (back-testing and model validation) and stress testing, which is required by European and US prudential regulators.
What are the challenges to AI risk management in banking?
Of course, there are some. Besides the technical challenges of developing AI apps for banking, such as building correct and relevant algorithms, there are also challenges related to regulatory field and data access rights.
Fintech sector is governed by strict compliance to regulations related to data. Data breaches are costly and the new legislation, for example, GDPR in European Union, provides serious liability for the companies dealing with personal data. Moreover, there is a number of regulations governing the financial institutions in the United States and Europe, such as FINRA, MiFID, and EMIR.
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