How Artificial Intelligence Is Impacting Financial Services Banking Analytics

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    • Tushar SonalInsights Explorer
      If data is oil, then analytics is the combustion engine of this current era.
    • BFSI
    • Data Science
    • Tech Trends

    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 technologies have quickly emerged & occupied the headlines that used to be reserved only for century-old behemoths.

    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.

    Banks and financial institutions love data. They record a vast amount of well-labelled 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 organizations work on sensitive financial data.

    Fraudulent transactions remain a major cost for banks. More than 2bn dollars lost 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.

    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, the maker of the AI-powered Zest Automated Machine Learning (ZAML) platform, helps companies assess borrowers with little to no credit information or history and provides automated underwriting solutions predicting risk better and reducing losses.

    A similar platform and an industry-leading tool for the same is 'Datarobot'. The platform offers pre-built algorithms, functions and automated machine learning solutions, o build more accurate credit models, making it convenient for companies in all sectors to harness machine learning and deep learning capabilities with AI technology.

    Banks and financial services companies have access to large transactional, behavioural 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, financial institutions are able to tailor-fit hyper-personalized offers for their customers.

    Curious to know more uses cases in Analytics for banking?

    Read how you can manage credit risk, increase sales revenue, and create value with Analytics in Banking and Financial services

    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, which can improve customer experience and reduce the cost of customer service at the same time.

    AI can help banks reduce call centre 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 manage demanding customer queries.

    Wells Fargo has recently been testing a chatbot on the 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.

    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 is the likelihood of the stock outperforming the market.

    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.

    Banking and Financial Services Analytics solutions

    Need an Analytics solution that will suit your specific business needs? Choose from our offered services and Contact us.

    AI and Machine Learning offer 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.

    Organizations are considering data as one of their essential assets and – if it is used wisely, then it will provide profits to all parts of the business from- financial planning to Sales & Operations, HR, etc.

    With the rapid evolution in the field of Business Intelligence, making information more available, usable, understandable is an exercise to simplify the end-user experience. Today, most of the data is in bulk, it is not an easy task for the managers and executives to process.

    About Author

    automated machine learning solutions
    Tushar Sonal

    Insights Explorer

    If data is oil, then analytics is the combustion engine of this current era.

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    • BFSI
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