AI powered decision making in banks

AI powered decision making in banks

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  Artificial Intelligence (AI) powereddecision making for the bank of the future Pankaj Baid 1
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  2 Banks that leveragemachine-learning models have potential to increase value by SOURCE: Multiple Sources from Internet Stronger customer acquisition Higher customer lifetime value Lower operating costs ▪ Banks gain an edge by creating superior customer experiences with end-to-end automation and using advanced analytics to craft highly personalized messages at each step of the customer acquisition journey. ▪ Banks can increase the lifetime value of customers by engaging with them continuously and intelligently to strengthen each relationship across diverse products and services. ▪ Banks can lower costs by automating as fully as possible document processing, review, and decision making, particularly in acquisition and servicing Lower credit risk ▪ To lower credit risks, banks can adopt more sophisticated screening of prospective customers and early detection of behaviors that signal higher risk of default and fraud
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  3 Banks can benefitfrom organizing their automation efforts around these significant elements Leveraging advanced analytical / machine learning models for automated, personalized decisions across the customer life cycle Building and deploying advanced analytics and machine learning models at scale Augmenting advanced analytical models with capabilities1 to reduce costs, streamline customer journeys, and enhance the overall experience Building an enterprise wide digital-marketing engine to translate insights generated in the decision- making layer into a set of coordinated messages delivered through the bank’s engagement layer 1 Next generation technologies like Natural Language Processing (NLP), facial recognition, block chain, Robotic process automation and behavioural analytics 2 AA – Advanced Analytics, ML – Machine Learning
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  4 4 4 4 4 4 4 4 4 4 • This isconsolidated survey responses from more than 700 senior decision makers across the accounting, banking, financial services, investment and insurance industries in the United States, United Kingdom, continental Europe and Asia. • About a quarter of all respondents indicated that they use Predictive Analytics or Data Mining / Analytics, with respondents from the US and UK leading the way. • Meanwhile, about one in seven (15%) of respondents signaled that they are using AI for Robo Advisory, a new class of tools that manage assets with minimal human intervention. Primary Applications of AI (% responses) SOURCE: Goodwin’s Fintech 2020, A Global Survey on the State of Financial Technology Technologies which are important today for the large banks (2020 Fintech survey results) 6% 7% 8% 15% 17% 23% 24% Biometrics Quant Trading Other Robo Advisory Lending/Credit Data Mining Predictive Analytics Regional Highlights (% responses) 14% 14% 28% 28% Asia Continental Europe UK US
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  5 5 5 5 5 5 5 5 5 5 SOURCE: Mckinsey Analytical techniquesfor various problems in the banks (Survey results) • This illustrates the relative total value of these problem types across Mckinsey database of use cases, along with some of the sample analytics techniques that can be used to solve each problem type. • The most prevalent problem types are classification, continuous estimation, and clustering, suggesting that developing the capabilities in associated techniques could have the widest benefit • Some of the problem types that rank lower can be viewed as subcategories of other problem types—for example, anomaly detection is a special case of classification, while recommendations can be considered a type of optimization problem—and thus their associated capabilities could be even more relevant 0 14 9 19 17 16 37 44 7 1 8 6 21 39 29 29 Essential Relevant Problem Types Sample Techniques Classification Logistic Regression Continuous Estimation Linear Regression Clustering K-Means Optimization Genetic Algorithms Anomaly Detection K nearest neighbors Ranking Ranking SVM Recommender systems Collaborative Filtering Data generation Markov Models Total AI value potential that could be unlocked by problem types as essential versus relevant to use cases (%)
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  6 Banks should prioritizeusing advanced analytics (AA) and machine learning (ML) in decisions across the customer life cycle 1 VAR is value at risk, 2 Non Performing Asset 3 AUM is assets under management Customer acquisition • Personalized offers • Customer retargeting • Propensity to buy scoring • Channel mapping Credit Decisioning • Credit qualification • Limit assessment • Pricing Optimization • Fraud prevention Monitoring and collections • Early-warning signals • Probability of Default • VAR- based customer segmentation1 • Agent-customer mapping Deepening Relationships • Intelligent offers • Churn reduction • Channel propensity • Fatigue rule engine Smart servicing • Servicing Personas • Dynamic customer routing • Real time recommendation engine • AI- enabled agent review and training Monthly customer acquisition run rate Credit approval turnaround time, % of applications approved Average days past due, NPA2 Deposit/AUM3 attrition rate Net promoter score, cost of servicing Key Metrics
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  7 7 7 7 7 7 7 7 7 7 SOURCE: https://www.is650agoodcreditscore.com/fico-credit-score-chart/ AI isbecoming critical as banking frauds are on the rise in the Indian Banks • The credit score chart below is based on FICO’s data and shows what percentage of the population fall into certain FICO score ranges • Delinquency rates are higher around 61% with consumers having FICO scores 599 and lower and 28% delinquency rates for consumers having FICO scores 599-699 and 8% with scores 700- 749 and 3% in the rest • Advanced Analytics is gaining popularity in domains like fraud detection, KYC analytics, credit monitoring and collections in banks 4.9% 7.6% 9.4% 10.3% 13% 16.6% 18.2% 19.9% Scores ranging From Very Bad to Excellent 61% delinquency rate 28% delinquency rate FICO Credit Score Range: 300 - 850 8% delinquency rate
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  8 8 8 8 8 8 8 8 8 8 SOURCE: https://www.mckinsey.com/business-functions/risk/our-insights/the-investigator-centered-approach-to-financial-crime-doing-what-matters Case Study– Low performance risk rating models without advanced analytics should not be allowed into production • This represents a typical multifactor customer risk-rating model for the retail business of a large North American universal bank • A manually conducted expert review of the results revealed that for every 100 customers rated high risk, 72 were actually medium to low risk; furthermore, 57 of every 100 customers rated medium to low risk by the model proved on review to have a high-risk profile • To put this into perspective, a credit- risk model with this kind of performance would never be allowed into production 100 72 57 85 High risk customers according to customer risk rating model "Low risk" cases removed (false positives) "High risk" cases added (false negatives) High risk customers after expert review High risk customers sent to enhanced due- diligence units (disguised real data example), indexed to 100
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  9 9 9 9 9 9 9 9 9 9 1 Suspicious ActivityReport SOURCE: https://www.mckinsey.com/business-functions/risk/our-insights/the-neglected-art-of-risk-detection Case Study – Bank used enhanced data and analytics to dramatically reduce the money laundering activities • At one large US bank, the false-positive rate in anti–money laundering (AML) alerts was very high. The remedial process involved a two-stage investigation. One team would determine whether an alert was truly triggered by suspicious activity. It would eliminate clearly false positives and pass on the remainder to experts for further investigation. Very few suspicious-activity-report filings resulted. • The bank rightly felt that this elaborate procedure and meager result was overtaxing resources. To improve the specificity of its tests so that AML expertise could be better utilized, the bank looked at the underlying data and algorithms. It discovered that the databases incompletely identified customers and transactions. By adding more data elements and linking systems through machine-learning techniques, the bank achieved a more complete understanding of the transactions being monitored. • It turned out that more than half of the cases alerted for investigation were perfectly innocuous intracompany transactions. With their more sensitive database, the bank was able to keep the process from issuing alerts for these transactions, which substantially freed resources for allocation to more complex cases Before enhanced data and analytics,% After enhanced data and analytics,% 0 90 10 8 2 100 50 45 5 3 2 100 Total alerts Known intra- company transfers Reviewed by primary team and closed Reviewed by secondary team Closed by secondary team Filed as SAR 1
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  10 Advanced Analytics inCredit Decisioning Machine learning models to automate the process for determining the maximum amount a customer may borrow. These loan-approval systems, by leveraging optical character recognition (OCR) to extract data from sources such as bank statements, tax returns, and utilities invoices, can quickly assess a customer’s disposable income and capacity to make regular loan payments. Analytics models can use their decisioning capabilities to quantify the customer’s propensity to buy according to the customer’s use of different types of financial products. Some even leverage natural-language processing (NLP) to analyze unstructured transcripts of interactions with sales and service representatives. AI-driven credit decisioning can build the business while lowering costs. Sharper identification of risky customers enables banks to increase approval rates without increasing credit risk. Advanced analytical models can predict fraud related instances. Limit Assessment Pricing Fraud Management SOURCE: EY Global - https://www.ey.com/en_gl/consulting/how-data-analytics-is-leading-the-fight-against-financial-crime
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  11 The combination ofAI and analytics enhances the onboarding journey for each new customer •Joy’s landing page shows her personalized offers: personal loan for travel, 5% off on travel insurance Analytics-backed hyper personalized offers based on customer microsegment •Joy receives a WhatsApp reminder for personal loan for travel at zero processing charges Propensity-to-buy model to identify whom to retarget. Channel propensity to identify right outreach channel •Joy receives a call to assist in journey; caller also informs Joy about custom travel services Analytics-enabled customer–caller mapping with specific cues provided to caller •Joy completes a streamlined 3-click journey to see the offer terms and conditions AA-enabled real time credit underwriting, limit assessment, and pricing •Joy completes online KYC form, provides details for employment verification, and sets up an online payment mandate AI capabilities to conduct relevant fraud checks (eg, facial recognition with KYC docs) •Loan disbursed to Joy; curated catalogue of offers ahead of Joy’s travel sent by email Hyper-personalized cross-sell and upsell offers Name: Joy Age:32 years Occupation: Working professional Family: Married, no children Profile attribute: Avid traveler
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  12 Artificial Intelligence inMonitoring & Collections comes to rescue to reduce NPA in banks Treatment Strategy ▪ Customers with high willingness but limited ability to pay in the short term may require restructuring of the loan through partial-payment plans or loan extensions. ▪ In cases where the customer exhibits both low willingness and limited ability to pay, banks should focus on early settlement and asset recovery. ▪ Advanced analytics, enabled by unstructured internal data sources such as call transcripts from collections contact centers and external data sources such as spending behavior on other digital channels, can improve the accuracy of determinations of ability and willingness to pay. ▪ To determine an appropriate contact strategy for customers at risk of default, banks can segment accounts according to value at risk (VAR), which is the loan balance times the probability of default. ▪ This allows banks to focus high-touch interactions on borrowers that account for the highest VAR; banks can then use low-cost channels like telephoning and texting for borrowers posing less risk. ▪ Banks have used this approach to reduce both the cost of collections and the volume of loans to be resolved through restructuring, sale, or write-off. (detailed in the next slide) Contact Strategy AI helps build a 360-degree view of a customer’s financial position, helping banks to recognize early-warning signals that a borrower’s risk profile may have changed and that the risk of default should be reassessed.
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  13 AI and MLcan classify customers into microsegments for targeted interventions SOURCE: Ignacio Crespo and Arvind Govindarajan, “The analytics-enabled collections model,” McKinsey on Payments, August 2018, McKinsey.com True low-risk • Use least experienced agents provided with set scripts Absent minded • Use interactive voice message (segment will probably self-cure) Dialer-based • Match agents to customers; send live prompts to agents to modify scripts True high- touch • Focus on customers able to pay and at high risk of not paying Unable to Cure • Offer debt restructuring settlements early for those who truly underwater Onscreen prompts guide agent–client conversation based on probability of breaking promises 10% of time saved, allowing for reassignment of agents to more difficult customers and specific campaigns Matching and prompts can increase sense of connection and likelihood of paying Added focus addresses higher probability of default rates in this segment Customer Type Description Impact Significant increase in restructuring & settlements increases chance of collecting at least part of debt
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  14 Artificial Intelligence innurturing customer relationships to maximize customer value Strong customer engagement is the foundation for maximizing customer value, and leaders are using advanced analytics to identify less engaged customers at risk of attrition and to craft messages for timely nudges. Deepening Relationships ▪ As with any customer communication in a smart omnichannel service environment, each personalized offer is delivered through the right channel according to the time of day. ▪ Deeper relationships are predicated on a bank’s precise understanding of a customer’s unique needs and expectations. A bank can craft offers to meet emerging needs and deliver them at the right time and through the right channel. By doing so, the bank demonstrates that it understands customers’ current position and aspirations. ▪ For example, by analyzing browsing history and spending patterns, a bank might recognize a consumer’s need for credit to finance an upcoming purchase of a household appliance. ▪ Ping An, for example, has developed a prediction algorithm to estimate the ideal product- per-customer (PPC) ratio for each user, based on individual needs. ▪ AI-powered decisioning can enable banks to create a smart, highly personalized servicing experience based on customer microsegments, thereby enabling different channels to deliver superior service and a compelling experience with interactions that are simple and intuitive. ▪ Banks can support their relationship managers with timely customer insights and tailor-made offers for each customer. They can also significantly improve agents’ productivity with streamlined preapproved products crafted to meet each customer’s distinct needs. ▪ Models that analyze voice and speech characteristics can match agents with customers based on behavioral and psychological mapping. Similarly, transcript analysis can enable prediction of customer distress and suggest resolution to the agent. Servicing and engagement
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  15 Augmented AA/ML modelswith cutting edge capabilities NLP – Natural Language Processing The rapid improvement of AI-powered technologies spurs competition on speed, cost, experience, and intelligent propositions. To maintain its market leadership, an AI-first institution must develop models capable of meeting the processing requirements of edge capabilities, including natural-language processing (NLP), computer vision, facial recognition, and more. Some edge technologies already afford banks the opportunity to strengthen existing models with expanded data sets. • Many interactions with customers — via telephone, mobile app, website, or increasingly, in a branch begin with a conversational interface to establish the purpose of the interaction and collect the info required to resolve the query or transfer it to an agent. • A routing engine can use voice and image analysis to understand a customer’s current sentiment and match the customer with a suitable agent. • The models underpinning virtual assistants and chatbots employ NLP and voice-script analysis to increase their predictive accuracy as they churn through vast unstructured data generated during customer-service and sales interactions. • While each customer-service journey presents an opportunity to deepen the relationship with the help of next-product-to-buy recommendations, banks should constantly seek to improve their recommendation engines and messaging campaigns (details on next slide) Description of an analytical use case in a customer-service journey AI enabled Use Case Cutting edge capabilities deployed as part of an enterprise strategy to enhance the AI bank’s value proposition have the potential not only to improve credit underwriting and fraud prevention but also to reduce the costs of document handling and regulatory compliance
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  16 Cutting edge capabilitiesenhance customer-service journeys 1 Interactive Voice response 2 Natural language processing enabled Digital self-cure channels for customers (eg, WhatsApp, mobile app, website) • Customer may seek immediate resolution of queries through digital self-service channels Voice-recognition enabled IVR,1 frontline bots handling 30–50% of queries • If query is not resolved through automated channels, customer may contact bank via chat or request call with live agent AI-enabled customer profiling, customer–agent matching • Customer connected to the appropriate agent (chat or call) based on type of query and customer profiling Voice-analytics and NLP- enabled2 customer sentiment analysis • Contact-center agents supported by live feedback and prompts to sustain superior customer experience AI-enabled service-to- sales engine • Customers are prompted for any specific offers preapproved for them Feedback loop via engagement channels • Post-call feedback and automated follow-up occur via digital channels
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