How Artificial Intelligence is Impacting Financial Services & Banking AnalyticsTushar Sonal
Artificial intelligence is creating an explosion of interest among the Financial Services & Banking companies. By delivering smarter applications that can simulate human intelligence, AI is rapidly disrupting existing business models and transforming every function- credit, operations, to customer service. Over the last decade, niche startups using AI/ ML technologies have quickly emerged & occupied the headlines that used to be reserved only for century-old behemoths.
The Artificial Intelligence (#AI ) in #BFSI Market is set to grow from its current market value of more than $2.5 billion to over $25 billion by 2024; as reported in the latest study by Global Market Insights.https://t.co/5aPHFmnWs6@GMI_US #ArtificialIntelligence #CAGR #IoT pic.twitter.com/fnovVj7TCd
— Sailotech (@Sailotech) June 12, 2018
Hence, there is tremendous excitement about the kind of possibilities and opportunities that AI brings to the financial services industry. But are financial services companies really prepared to capitalize on the opportunity? The industry has usually been one of the earliest adopters of technology breakthroughs, and AI/ ML-backed automation & cutting-edge innovations are already offering a critical competitive advantage determining the winners & laggards. With 72 percent of financial professionals seeing AI as a problem solver in the industry, financial companies are ramping up their AI investments. But, large scope remains to be mined & uncovered. Let us take a look at some of the top AI/ ML use cases in finance that are driving the change in the financial sector today.
How is AI changing Financial & Banking Services Companies?
Banks and financial institutions love data. They record a vast amount of well-labeled data for purposes such as regulatory and fraud detection requirements. They also process extensive external data procured from third-party sources such as those related to credit history. This vast goldmine of customer & transactional data accelerates analytics and machine learning possibilities for these organizations. AI/ ML algorithms’ accuracy can be directly correlated to the amount of input data. Security is a critical concern since the organizations work on sensitive financial data.
Increasing fraud prevention and security-
Fraudulent transactions remain a major cost for banks. More than 2bn dollars loss due to fraud is suffered by banks annually in the USA alone. Cyber frauds are spiking in frequency and severity around the globe. Neural networks, with their remarkable ability to derive meaning from complicated or imprecise data, can be used to detect fraudulent trends that may be too complex to be noticed by either humans or other computer techniques. This has wide-scale applications in the identification of potential fraud and prescribing corrective action on them before they become too severe. Machine learning platforms can be used to analyse network data, creating probability-based calculations and detecting suspicious activity before it can cause damage for financial firms.
Improved risk management-
AI-based credit risk assessment is being adopted by banks to better gauge the chances of default. Machine learning and AI algorithms are able to take a lot of varied factors as input and provide a score that signifies the probability of default. For example, ZestFinance is the maker of the Zest Automated Machine Learning (ZAML) platform, an AI-powered underwriting solution that helps companies assess borrowers with little to no credit information or history.
Optimized marketing campaigns with better targeting-
Banks and financial services companies have access to large transactional, behavioral and demographic data on consumers. AI/ ML algorithms can quickly sift through this data to segregate and create categories. Then banks can optimize their campaigns with well-targeted, timely and relevant offers. Banks are therefore able to drive higher ROI on their marketing spends and develop meaningful engagement with their customers. Every customer’s needs and habits are different from others. With AI-based customer-centric models, the financial institutions are able to tailor-fit hyper-personalized offers for their customers.
Improved customer experience –
Banks incur a lot of cost on customer services such as managing customer queries and complaints. Digital assistants such as conversational bots with AI technologies have become better at interpreting human language and interacting with them intelligently. can improve customer experience and reduce the cost of customer service at the same time. It will help banks reduce call center volume by providing customers with self-service options and solutions. Additionally, the AI-powered chatbots also give users calculated recommendations and help with other daily financial decisions. For large corporations, this will help take the load of managing regular customer complaints and queries off from the frontline team and enable them to devote more effort and time to managing demanding customer queries. Wells Fargo has recently been testing a chatbot on Facebook messenger platform that can reply to customer queries instantly such as information on account balance, ATM location. This promises to make the institutions more customer-centric, delivering a holistic experience.
Efficient cost management and reduced cost of operations-
With AI technologies, companies are discovering newer and cost-effective ways to manage business operations. Regulatory audits are a big part of financial services. Robotic Process Automation driven by AI technology can augment human efforts to parse and read through voluminous contract documents. This has far-reaching implications for the whole industry. And help save time and effort while augmenting human efforts in the whole process.
AI-powered computers can analyse large, complex data sets faster and more efficiently than humans. The resulting algorithmic trading processes automate trades and save valuable time.
One of Kavout’s solutions is the Kai Score, an AI-powered stock ranker. The Kai Score analyses massive amounts of data, such as SEC filings and price patterns, then condenses the information into a numerical rank for stocks. The higher the Kai Score, the more likely that the stock will outperform the market.
— JasperColinResearch (@jaspercolin_res) May 23, 2017
Today, customers demand smarter, more convenient and safer ways to access, spend, save and invest their money This ability to, in a sense, see the future is a huge potential boon for financial institutions to drive more productive business operations, better customer engagement across more platforms, and next-level risk identification and compliance response
This offers a tremendous opportunity for financial companies to offer to provide best-in-class customer services, cut down on administrative and operational costs related to business, identify and nip frauds before they have severe repercussions and make winning strategies for revenue growth.