One of the biggest hurdles faced by banks implementing AI is to move from experimentation to select use cases and then to scaling AI technologies across the organisation. Reasons include the absence of a clear strategy for AI, a rigid and investment-starved technology core, fragmented data assets and traditional operating models that hamper collaboration between business and technology teams.
Most banks (80%) are highly aware of AI's potential benefits, per an OpenText survey of financial services professionals. Many banks are planning to deploy solutions enabled by AI: 75% of respondents at banks with over $100 billion in assets say they're currently implementing AI strategies, compared with 46% at banks with less than $100 billion in assets, per a UBS Evidence Lab report seen by Business Insider Intelligence.
According to Stu Chalmers, Winjit Associate VP for Sales and Business Development in South Africa: "Several banks are using AI to transform the customer experience by enabling seamless, 24/7 customer interactions – but AI in banking applications isn't just limited to retail banking services. AI can also benefit the back and middle offices of investment banking and all other financial services for that matter."
AI can lead to higher automation and, when deployed after controlling for risks, can often improve human decision-making in speed and accuracy. According to several studies, AI can help boost revenues through increased personalisation of services to customers and employees; lower costs through efficiencies generated by higher automation, minimise errors rates and better resource utilisation; and unleash new and previously unrealised opportunities based on an improved ability to process and generate insights from vast, valuable data.
Chalmers says: "AI is rapidly reshaping the business landscape of the financial industry. Hopes are high for increased transactional and account security, especially as blockchains and crypto-currency adoption expand. In turn, this might drastically reduce transaction fees due to the lack of an intermediary."
Digital ecosystems are disintermediating traditional financial services. By enabling access to a diverse set of services through a common access point, digital ecosystems have transformed the way consumers discover, evaluate and purchase goods and services.
One hindrance in the adoption of AI could be the perceived complexity and expenses of the exercise. A requirement to have data clean and in central point, employing data scientists to undertake analysis, build and test models, then having the flexibility to react to a model’s outcome sounds like a difficult and expensive journey. However, the deployment of AI solutions has been simplified by tools that give those with domain knowledge and not necessarily data scientists or statisticians the ability to undertake most if not all of the required tasks. The key is doing something with the outcomes and improving processes.
Seeing the need in the market, Winjit has gone further, continuously raising the bar on personalisation, to the point where we sometimes anticipate customer needs before the customer is aware of them and offer highly tailored services at the right time through the right channel.
Winjit's PredictSense is an end-to-end machine learning platform powered by AutoML to create AI-powered analytical solutions. Drag-and-drop low code functionality gives the layman a tool to be able to generate machine learning models and deploy them in to live databases.
Winjit focuses on the latest trending technologies, including artificial intelligence and machine learning, the Internet of things, blockchain and fintech, product engineering and mobile applications. We provide end-to-end solutions from conceptualisation and optimisation to providing real-time solutions by developing software systems for any business. We are proud to contribute, with our tailored, innovative solutions, to transform our society for a better future.
To see a demo of PredictSense or learn more, contact us at marketing@winjit.com or call (+27) 10 109 1507.
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