AI News, Artificial Intelligence – The New Frontier in Banking, Part 2: Extending Through the Payment Ecosystem – by Maria Schuld
Artificial Intelligence – The New Frontier in Banking, Part 2: Extending Through the Payment Ecosystem – by Maria Schuld
Focused on applications beyond fighting fraud, this is the second article of a two-part series on the deployment of artificial intelligence in banking.
Increasing relevancy of offers – a win for loyalty Artificial intelligence applications can conquer the challenge of rewarding loyal customers for their business in ways that are directly relevant to those individuals.
For instance, analysis of behavioral patterns and other customer data through AI can drive more personalized and relevant recommendations – from helping consumers improve their financial health to offering more targeted rewards that drive sales.
For example, a bank could give a millennial cardholder a meaningful discount for dining at a restaurant adjacent to an arena where they just bought tickets to a sporting event.
On the plus side, AI can help “credit invisibles” – consumers with little or no recent credit histories – gain access to credit through the use of alternative data, such as telecom or utility payments.
Using factual inputs like checking whether or not someone is in the military (under the Military Lending Act, which includes active-duty service members and covered dependents) or is on the FBI’s terrorist watch list (Terrorist Screening Database) is easy.
Use caution when creating the if/then analyses (e.g., If consumer A has consistently made car loan payments, then consumer A will consistently make a mortgage payment) that underpin AI algorithms.
The Use of AI in Banking is Set to Explode
The explosive growth of structured and unstructured data, availability of new technologies such as cloud computing and machine learning algorithms, rising pressures brought by new competition, increased regulation and heightened consumer expectations have created a ‘perfect storm’ for the expanded use of artificial intelligence in financial services.
The benefits of AI in banks and credit unions are widespread, reaching back office operations, compliance, customer experience, product delivery, risk management and marketing to name a few.
According to a survey conducted by Narrative Science in conjunction with the National Business Research Institute, 32% of financial services executives surveyed confirmed using AI technologies such as predictive analytics, recommendation engines, voice recognition and response.
This is driven by AI’s ability to build knowledge at high speed, understand natural language, and run operational processes in a fully compliant fashion.
Personalized communications and advice as enabled by AI can be reflected by robo advisors – online wealth management services that provide automated, algorithm-based portfolio management advice without the help of a human counterpart.
With AI, algorithms can regularly rebalance the portfolios to maintain the original investment guidelines and operate at costs less than 100 basis points (compared to 2 – 3% for traditional brokers).
What once was a very tedious process of new customer onboarding communication can now become highly personalized interactions based on individual activity post opening.
Mobile banking apps like Moven and Simple let users track their spending and increase their savings with automated, personalized recommendations via a specialized debit card linked with their smartphone app.
Artificial intelligence will not.” Finie: The AI App for Your Bank Account As is the case for several innovations in banking over the past several years, one of the most exciting applications of AI in retail banking has come from a small start-up … not from a large national bank.
Ann Arbor-based Clinc has created a voice-powered AI platform, named Finie (for financial genie) as a way to interact with a banking account using natural language queries (as opposed to a very limited set of rule-based commands).
Instead of being limited to a command such as, “What is my balance”, Clinc’s Finie can be asked, “Do I have enough money to go out to dinner tonight.” Instead of, “Provide list of transactions”, Finie can be asked, “How much did I spend on groceries” or “Did I spend more on coffee this month than last.” Finie is integrated within a banks’ mobile banking application, acting as “a voice-activated intelligent personal assistant that is able to answer financial questions unique to each individual user, offer personalised spending advice, and fulfil any banking task”.
Jim Marous is co-publisher of The Financial Brand and publisher of the Digital Banking Report, a subscription-based publication that provides deep insights into the digitization of banking, with over 150 reports in the digital archive available to subscribers.
Financial Artificial Intelligence Transforms Credit-Rating Process
The banking business is employing financial artificial intelligence to assess the creditworthiness of borrowers, a development that’s allowing the industry to massively expand its reach, according to a report from analysis firm GlobalData.
“However, some lenders are now using AI to analyze non-traditional types of data, such as mobile phone usage and social media profiles, to predict the creditworthiness of borrowers.” The firm noted that traditional credit scoring techniques are becoming ineffective amid changing market circumstances.
“Although these consumers may not have access to regular banking services, many are heavy users of mobile phones and social media, and this generates huge amounts of data that can be analyzed to model their financial reliability,” said Daoud Fakhri, principal analyst for retail banking at GlobalData in a press-release quote.
“There is, therefore, huge potential to widen access to credit without exposing lenders to higher levels of risk.” Addressing customers in developing economies represents a huge opportunity for financial institutions.
The Five Habits of Successful Data-Driven Financial Institutions
Comments Even if your bank or credit union isn't ready to deploy chatbots, virtual assistants, artificial intelligence (AI) or innovations involving the Internet of Things (IoT), the importance of data and advanced analytics capabilities has never been greater.
By Jon Ogden, Director of Content at MX If you’ve been following the financial services scene lately, chances are high that you’ve seen plenty of talk about chatbots, virtual assistants, and other forms of artificial intelligence (AI).
In fact, the Harvard Business Review has shown that only 3% of companies’ data meets basic quality standards, revealing that there’s an enormous business advantage for those companies that can get their data strategy right and effectively execute on it.
To fix this problem, banks and credit unions should first consider hiring professional data analysts who can ensure that the key data components are accurate.
The key benefit here is that with greater insights into your business you’ll have a greater ability to implement the exact strategies you need to stand apart from your competitors.
In this way, a user who has surplus money in a savings account might get a suggestion to move that money to a term deposit to ensure a higher savings rate, while a user who has a credit card with a competitor will see an offer to switch that card to your institution and get a lower APR. As each user realizes that their digital experience is being tailored to meet their exact needs, their trust for your institution deepens.
Bit by bit, you earn a reputation as a bank or credit union that can actively provide a service that your users didn’t even know they needed — a service along the lines of Amazon’s “Recommended for You” feature.
Banking Needs Deeper Customer Insights to Remain Relevant To combat this potential problem, it’s critical to never sell user data to third parties — especially without the explicit approval of each individual user.
In addition, if someone walks into the branch to get help, your tellers and service representatives should be able to access the same data points and insights that are being leveraged by the chatbots.
Data is the Foundation for the Future of Banking Whether or not your bank or credit union decides to explore advanced technologies such as chatbots, virtual assistants, artificial intelligence or the Internet of Things (IoT), it’s crucial to keep in mind that all future solutions rely on a solid foundation of accurate data.
Beyond a review of goals and investments, this report delves deeply into the strategies, effectiveness, challenges and measures around improving the use of data and analytics to improve the customer experience in the banking industry.
Synechron - Artificial Intelligence
Artificial Intelligence (AI) has been around for decades but a convergence of Big Data, increased computing capabilities, and user demand has created a perfect storm where AI applications are evolving at a rapid pace.
Neo uniquely brings together Synechron’s digital, business consulting and technology capabilities, allowing financial institutions to deploy cutting-edge AI solutions that solve complex business challenges using Natural Language Processing (NLP), Chatbots, Robotic Process Automation (RPA), Cognitive Machine Learning, Data Science, and Robo-Advisor for financial services.
How AI apps for banks are changing the face of the financial sector
Long before chatbots popped up as interesting business-use cases, long before mobile banking applications offered military-grade secure transactions, and much before focused analytics tools for banking made themselves known, AI apps for banks augmented by machine learning and deep learning began creating an impact in the world of banking.
Artificial intelligence is already playing a role in critical finance and banking functions such as loan approvals, asset management, portfolio design, and risk management.
These applications help banks build an online accessible tool that considers user preferences, personal demographic information, earning power, and wealth sources, and then matches these with their financial goals.
In parallel, these systems take real-time market data and factor in parameters like a customer’s credit history, risk aversion, and lifestyle practices, creating a very robust portfolio of investment and saving instruments across asset classes.
This not only helps end users quickly get vital inputs on suitable financial products, but also helps banks market and sell the most appropriate products to users.
Better product recommendations and personalized advice Extending the idea of personalized investment portfolio suggestions, there’s the more generic concept of personalized financial advice, which is expected to go big very soon.
These AI-based applications can integrate with a user’s online bank accounts, debit and credit cards, and e-wallets to track their expenses, present advice on better expense management practices, and help them choose more suitable financial products that sit well with their financial habits, liquidity requirements, and short-term saving goals.
However, easily available computing power, storage of massive proportions of entire business data on public cloud, outpacing of learning in the cybercrime world as compared to cybersecurity world, all combine to create the “perfect storm” potential for these organizations.
AI-based fraud detections tools of today leverage the principles of deep learning and machine learning, and are already beginning to incorporate the benefits of neural learning, and hence have tremendous potential in cybersecurity, particularly for the banking sector.
In scenarios were legal compliances, process updates, and new product designs necessitate changes on communications and workflows, AI tools help implement the changes in near real time.
Critical technological investment With strong potential in cost reduction, personalized service delivery, and fraud detection, AI has become a critical technological investment for the banking sector.
- On 16. juni 2021
Machine Learning, AI, and Big Data | Neil Jacobstein | Exponential Finance
Join Neil Jacobstein as he explores how machine learning and data analytics are revolutionizing credit, risk and fraud, and changing the overall financial services landscape. Exponential Finance...
Artificial Intelligence Machine Learning Big Data | Neil Jacobstein | Exponential Finance
How machine learning and data analytics are revolutionizing credit, risk, and fraud, and changing the overall financial services landscape.
2016-2017 AI trends in Financial Services
Baker & McKenzie partner, Astrid Raetze, believes that financial institutions are looking at the various applications of artificial intelligence from risk management to credit assessment. AI...
AI and Machine Learning in Banking
This is a talk I did recently at Microsoft about FinTech, RegTech, AI, machine learning and data in banking. 1:10 Introduction to FinTech 3:57 Democratising AI 6:15 AI == Innovation 8:03 Compute...
Fintech Startups Are Disrupting Traditional Lending
Prosper and other fintech startups are disrupting traditional lending. On stage at the Future of Fintech Conference ( CB Insights CEO..
Machine Learning, AI, and Big Data | Neil Jacobstein | Exponential Finance
Brendan Herger, Data Scientist at Capital One Financial Corporation Alex Aguilar, CTO of Overland Advisors Conor Jensen, Analytics Program Director at Zurich . Join Neil Jacobstein as he...
Artificial Intelligence Powered Banking Jul 19 2017 9:00 PM IST
More Details- AI technologies such as machine learning, deep learning, predictive/prescriptive analytics, virtual agents and natural language understanding technologies..
Application of Machine Learning in Financial Sector | Machine Learning and AI in Financial Services
Learn More: Beginners guide to Machine Learning in the financial sector. In the webinar learn the use of data science in Banking and Financial Services. Know More about..
Machine Learning: Data in Financial Services
Speaker: Marley Gray, Microsoft Machine Learning and Cortana Analytics presents new opportunity for Financial Institutions to enhance the way advisors, brokers, traders, treasurers or anyone,...
Ways Banks are using AI that you didn’t Realize
Banks are using Artificial Intelligence techniques such as natural language processing, robotics, data science, cognitive learning and more to solve business challenges. Find out different...