CAN you BANK on AI?- AI and the BFSI Sector
Blog: NASSCOM Official Blog
“The banks are providing extended services. They’re bringing their vans and providing basic ATM and other banking solutions within the society”, my mother proclaimed in a (COVID) crisis-struck world feeling gifted during the lockdown.
The solution came to her door. I was glad, she could manage.
We’ve been forced to embrace less human contact, often succumbing to alternate means of functioning from what was considered normal.
Could we have done better with AI powered technology?, I wondered. Maybe yes!
Djingo(2018), powered by IBM Watson was a virtual adviser developed by Orange Bank answering customer queries and providing basic banking services.2
Technology experts consider this to be a turning point in the history of the BFSI sector. Personalized banking and fraud prevention could be two major areas where Artificial Intelligence and Machine Learning can percolate within the sector.
AI will have a huge impact across the sector due to its capability of narrowing down huge data and eventually getting personal, transactional and business information.1 Some key AI touchpoints within the BFSI sector include:
Image by Franck V
1.Robo-Advisers & Virtual Agents- Remember ‘Chitti, the Robot’ from Rajnikanth’s film Enthiran(2010) using his intelligence to crack simple solutions to complex problems. Imagine having your own version of him advising you in making financial decisions. Or better still, having virtual agents that help automate insurance underwritings.
2. Curbing Fraudulence– Just like the Netflix ML movements tracking likeability and building personal databases, ML in the banking sector can create user specific behaviour profiles. Transactions may be recorded and later matched with these profiles to understand, prevent and curb fraud transactions.
3. Personalization– AI can analyze user data basis user touchpoints at various levels and proactively enhance user experiences. It’s almost like Shiva’s third eye capturing additional data points from users to offer customized solution in addition to efficiently processing finance and credit related decisions for customers.
Case in point- Advice Robo is a classic example of an organization combining data in AI driven psychographic and risk management. It utilizes ML to assess credit worthiness using psychometric credit scoring solutions and provides loans to thin files (i.e people with no credit history). The solution contains 22 questions taking less than 5 mins to complete.
Image by
Anastasiia-Ostapovych
That said, adopting AI can come with a series of challenges:
1.Managing Data– Lack of interpretability and auditability of AI and ML methods can be risky especially while dealing with heaps of data. A further challenge would be to train the deep learning systems to structure the given data.
2. Trust- Blackbox AI can lead to trust issues among stakeholders. Moreover, adopting AI without proper preparation may also give rise to third party discrepancies. All these together leading to trust and transparency issues.
3. Unbiased Data- Adequate testing and training of tools with unbiased data can be a huge challenge since ML concludes decision making basis digital traces of human activities. These activities may reflect human prejudice and biases and may discriminate users based on caste, class or ethnicity.3
The banking sector has been using data to derive insights about customer behaviour and provide enhanced experiences. AI and ML can be gamechangers for the sector to process this information if adopted well and with adequate preparation.
For all you know, my mother may call up another day proclaiming she has her own personal robo-adviser.
From assisting in risk assessments to providing and matching lender-borrower profiles, the rising adoption of AI in the banking sector will definitely have a positive impact in the market.
Read the full report for further details on how AI can significantly impact key industrial sectors.
What do you think ? Tell us in the comments below.
References:
- Global artificial intelligence (AI) market in BFSI sector 2019-2023 | 32% CAGR projection through 2023 | Technavio. (2020, January 14). https://www.businesswire.com/news/home/20200113005533/en/Global-Artificial-Intelligence-AI-Market-BFSI-Sector
- How AI and ML are strengthening data for the BFSI sector. (2019, June 14). Moneycontrol. https://www.moneycontrol.com/news/technology/how-ai-and-ml-are-strengthening-data-for-the-bfsi-sector-4099301.html
- Uncovering the true value of AI – Executive AI playbook for enterprises. (2019, December 27). NASSCOM. https://www.nasscom.in/knowledge-center/publications/uncovering-true-value-ai-executive-ai-playbook-enterprises
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