Cognitive Agent Driven Real-time Underwriting Decision Making
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Credit Underwriting practices and processes are dependent on changing human temperament and limited recollection of past underwriting decision intelligence for precision in lending, and expected repayment defaults.
Algorithm driven Cognitive Agents adjudicate customer character, payment capacity, collateral value and lending conditions by intensively studying and comparing numerous factors - a Big Data Initiative - in real-time to underwrite and continue to enhance intelligence for advanced strategies on Risk Appetite Based Portfolio Management and Customer Acquisition. |
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Banking of Things For Enhanced Experience With Product & Services
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Networked economy of devices holds the promise for accelerating both banking services and operations to the next level of seamlessness. Banking of Things (BoT) creates opportunity for new products, services and or business models. BoT could usher in an era where products are designed not by banks but led by customers. Customers would be incentivised to build a product or a service, say a home loan or savings account, and decide the interest rate, which in turn will be dependent on behavioural factors such as customers’ utility payment patterns or eating habits, new age indicators of characteristics such as prudence and health consciousness.
BoT could give rise to unique partnerships forged between banks. For instance, banks have their credentials in the consumer’s refrigerators, which is connected to the internet. In this model, the consumer pays for groceries through the refrigerator. Customers can be incentivized for using the bank’s app to make the payments, ultimately improving customer experience, loyalty factor and in turn the customer satisfaction (CSAT) scores. |
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Dynamic Robots For Financial Advice & Investment Management
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Robo-advisors provide financial advice or investment management services online with moderate to minimal human intervention based on mathematical rules and or algorithms. Combination of rules and algorithms are executed by an intelligent software, Bots or Robots, and thus financial advice do not require a human advisor. The software utilizes its algorithms to automatically allocate, manage and optimize clients' assets based on their appetite and investment interests.
Banks can offer real-time portfolio analytics services on a personalised basis to all of their high net worth clients through Robo Advisory tools and lower investment threshold for people to leverage banks investment expertise. |
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SME Business Success Driven By A Digital Ecosystem
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SME’s success is dependent on the ability to turn around its business in planned gestation periods with sustainable operations. Access to credit, markets, vendors and customer has a significant role in their existence. Banks that offer a platform for SME customers, a community of small sized enterprises by providing cloud-based tools, help SMEs manage, grow and promote their business through digital mediums.
Small and micro-businesses can use the digital ecosystem without any fee and without the necessity of being a bank customer. Additionally, SMEs can benefit from substantial discounts from third parties. This enables them to get prices and services which were typically out of the reach of smaller companies. |
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Customer Behaviour Intelligence For Personalized Banking Experience
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Analyzing personal and transaction data gives banks the opportunity to understand customers’ needs today and anticipate future ones. Personalization then adds the ability to deliver those insights to customers in a contextual manner, to increase sales targeting and effectiveness.
A low balance with upcoming bills might call for a personal overdraft offer, a high balance on a current account might suggest appetite for a fixed deposit, recurring visits to the mortgage loan information page might indicate plans to purchase a home, a frequent traveller may be interested in a travel insurance, a fine dining lover might appreciate discounts at a popular restaurant, etc. Opportunities to leverage customer-centric data analytics and personalization for targeted cross-selling or merchants-based campaigns behind the firewall are numerous. |
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Data Monetization for Product Innovation & Customer Engagement
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Bank to offer merchants more business insights through market intelligence, that packages credit and debit card information with geolocation, demographic, and other transactional data to enable merchants to develop new insights into their customers' behaviours. Data and micro-patterns that would have been difficult to identify without these insights.
Merchant clients in turn view and use their consumers' transaction histories to improve product development, inventory management, and set staffing levels. Customers would be willing to pay for these products, and the banks offering such services will have a sticky factor. Such services promise deeper customer engagement and improved retention for both banking and merchant service offerings. |
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Customer Behaviour Intelligence For Enhancing Customer Experience
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Customer journeys offer opportunities to learn from experiences. Changing spending behaviours can help banking institutions in proactively designing products that will suit the needs of customers and or run their operations effectively. Listening to customers at every stage of engagement, whether email, voice, video or through social channels, helps in articulating segments of customers basis their profile features.
Transaction and consumerism behaviours, blended with social engagement interests, strengthen the knowledge of product profitability, selling and mis-selling practices, conduct of business, delinquencies, collection strategies and incentivization tools for improving customer experience and engagement. |
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Insurance
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Computationally intense and assumption driven insurance underwriting models need replacement. Automated Underwriting Systems have the capability of underwriting policies in less than 8 minutes helping customers make timely decisions.
Machine learning models operate at speed, allowing formation and use of intelligent assumptions that are based on numerous data sources. Data, both structured and unstructured, is employed to study the potential for lowering the probability of damages. Cognitive Agents offer competitive opportunities for product differentiation, shifting the focus of management towards investment in prevention and early detection of insurance risks. |
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Personalized Customer Services
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“Our interactive menus have changed.”
Traditional Interactive Voice Response Systems (IVRS) are a good example of automation that fails to solve the problem of Satisfactory Customer Services & Resolution Handling. Customers have to survive the maddening process of choosing between services only to find an agent and to restart with personal authentication. Artificially Intelligent Bots armed with Speech & Image Recognition capabilities recognize customers within fractions of a second, while in parallel recollect past customer experience intelligence to interact and provide assistance. Cognitive Agents continue to learn and generate advanced Customer Behaviour Intelligence. With possible deployment choices between ‘Front-end AI-powered Bots’ and ‘AI-assisted Agents’ machines understand the complexity and urgency to undertake resolution actions or recognize customer emotions to direct calls to Customer Services Agents for a more Personalized Experience. |
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Customer Complaints & Resolution Handling
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Complaints are an expression of dissatisfaction made to an organization related to its products or services, or the complaints handling process itself, where a response or resolution is explicitly or implicitly expected. Rising counts of complaints and several varieties of issues require intelligence for problem identification and resolution classification. Dependent on employees operating under the control of Policies and strict adherence to Processes, the function fails to deliver resolution and satisfied customers.
Machines have capabilities to live rule based intelligence, complex process handling and information classification. Customer complaints are reviewed by Cognitive Agents for classification as per operations policy and resolution turnaround-time allocation. Resolution action begins at the very act of receiving a customer complaint and the renewed focus is on helping most dis-satisfied customers, while resolving issues at scale and precision. |
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Data Risk Profiling
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Increasing velocity, variety and volume of data in business presents a threat that an attempt to protect each and every aspect of data is neither a smart nor an economical way of implementing internal Cyber Security. Knowing what data sets carry higher risk and need most protection, would help concentrate risk management efforts and optimize data security investments. While ‘Data Classification’ Policies and Processes are there to control and monitor High Risk Data, they demand application of business rules to label data into different classes.
Artificially Intelligent algorithms analyze and classify data, same way humans do, and at higher scales of precision – above 95%. Assisted by Bots, Humans review the summarized information and validate exceptions to take ‘Data Classification’ accuracy to 99%. Cognitive Agents continue to add more intelligence with validation of trends and business optimizes Cyber Security Risk Management costs. |
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Cyber Threat Management
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"Cyber Threats Will Result In Crisis!"
Equipped with emerging technologies, Cyber attackers are moving at the same pace or faster than the victims concealing the patterns of their activities to depict legitimacy. These patterns continue to change or form newer strategies, failing the most advanced search and or fingerprinting systems to detect a Cyber Threat. Cognitive Agents solve the identification problem by learning not only patterns but the behavior of patterns through deep neural networks. Machines study these wide range of anomalies and changing patterns to quickly recognize threats at precision, even if the threat sits as one in a billion points in the log or traces of data. Cyber Security Teams can now invest in studying the beahaviour of machines to plan and invest in better Cyber Security Strategies and to protect from Cyber Threats before they strike business operations. |
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Employee Engagement
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Organizations collect data through surveys, questionnaires and interviews by questioning employees about their workplace experiences to assess their engagement. Challenge remains with the frequency of such events and historical event comparisons to form an intelligence opinion and plan improvement measures.
Speech and Image Recognition enabled Cognitive Agents approach the same problem for a smart solution by engaging employees into interesting discussions or events where they can share their unbiased views consciously and subconsciously with the machines to measure their emotion patterns to then blend psychometric principles on the emotion recognition data to successfully create unbiased and unparalleled insights on Employee Engagement. |
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