Artificial intelligence technologies are becoming increasingly embedded in daily life, necessitating widespread adoption of these tools by financial institutions to maintain relevance.
In 2016, AlphaGo beat 18-time world champion Lee Sedol in Go, a complicated board game. Since then, AI technologies have advanced and are transforming industries. AI-powered algorithms customize digital content suggestions, develop store clothing lines, and even surpass expert physicians in diagnosing cancer. McKinsey predicts AI could contribute $1 trillion annually to global banking.
Why must banks become AI-first?
Banks have used new technologies for decades to improve consumer interactions. In the 1960s, banks introduced ATMs and card-based payments. In the 2000s, internet banking became popular, followed by mobile “on-the-go” banking in the 2010s.
Few would deny that we’re in the AI-powered digital era, aided by dropping data storage and processing costs, greater access and connection, and rapid AI developments. After adjusting for hazards, these technologies can increase automation and enhance human decision-making speed and accuracy. As a result, AI can unleash $1 trillion in added value for banks yearly, one of the highest potentials across sectors.
AI technology can raise revenues by personalizing services to clients (and workers), cutting costs through more automation, reducing mistake rates and better resource utilization, and unearthing new and untapped possibilities by processing and generating insights from large data troves.
AI disrupts bank revenues, personalization, omnichannel experiences, and innovation cycles. Banks that aren’t “AI-first” risk losing clients to competitors. Current trends exacerbate this risk:
- Client expectations increase with digital banking- Internet and mobile banking rose 20 to 50% across nations during the COVID-19 outbreak. As the epidemic diminishes, this should continue. 15 to 45% of consumers plan to visit branches less after the crisis. 4 More digital banking consumers expect more, especially compared to top consumer-internet corporations. Digital experience professionals raise the standard of personalization by anticipating customer expectations and giving highly personalized services at the proper time and channel.
- Leading financial firms use AI- McKinsey’s Global AI Survey5 found that 60% of financial-services respondents have AI. Robotic process automation (36%), virtual assistants (32%), and machine learning (25%) are used for structured operational processes, fraud detection, underwriting, and risk management. Many financial services organizations employ AI episodically and for specialized use cases, but more banking leaders are integrating it from front- to back-office.
- Digital ecosystems decentralize finance- Digital ecosystems have centralized how people explore, evaluate, and buy goods and services. In China, WeChat users may book cabs, purchase meals, get massages, play games, send money, and get a credit line. Nonbanking enterprises and “super apps” integrated financial services and products into their client journeys, undercutting traditional banking operations. Banks must rethink their digital interactions and use AI to tap new data sources.
- Technology firms offer financial services- Leading tech companies have a vast and active consumer network, troves of data, innate talents in designing and implementing breakthrough technologies (including AI), and low-cost investment. Tech companies have aggressively entered adjacent sectors to find new revenue and retain users. Big-tech companies have a foothold in some financial services industries (payments, loans, and insurance) and may soon strive to expand and grow.
What might the AI bank of the future look like?
The AI-first bank will provide intelligent, tailored, and truly omnichannel offerings and experiences to match consumers’ growing expectations and defeat competitive challenges in the AI-powered digital era (seamlessly spanning physical and online contexts across multi-channel).
The AI-first institution will maximize operational efficiency by automating manual processes (a “zero-ops” approach) and replacing or augmenting human choices with sophisticated diagnostic engines like using accounting automation software. In addition, traditional and cutting-edge AI technologies, such as machine learning and facial recognition, will be used to analyze extensive, complex customer data in (near) real-time.
Future AI-first banks will have the speed and agility of digital-native firms. It’ll introduce new features in days or weeks, not months. It will work with partners to integrate new value propositions across routes, platforms, and data sets.
What obstacles prevent banks from deploying AI capabilities at scale?
Existing banks have two seemingly opposing goals. Banks require fintech-like speed, agility, and adaptability. They must also manage a typical financial-services firm’s scale, security standards, and regulatory obligations.
Few institutions successfully diffused and scaled Intelligent Automation technologies despite spending billions annually on change-the-bank technology programmes. The lack of an AI strategy is the biggest challenge for banks. Many banks have poor core technology, data backbone, and an outdated operating model and personnel strategy.
Built for stability, banks’ fundamental technological platforms enable payments and lending successfully. Before using AI at scale, banks must fix historical system shortcomings. These systems lack the capability and flexibility to meet closed-loop AI applications’ variable computing, data processing, and real-time analytic demands. Core systems are hard to modify and costly to maintain. Many banks’ data reserves are scattered among numerous silos (business and IT teams), and analytics efforts focus on stand-alone use cases. Without a centralized data backbone, assessing relevant data and providing timely recommendations or offers is very hard. If data is the bank’s raw material, it must be managed and kept safe to analyze at scale for millions of clients in (near) real-time at the “point of decision” across the company. To expand analytics and AI models, companies require a comprehensive set of tools and procedures to design, test, deploy, and monitor models.