Which skill would cost fintech companies the LEAST if their employees mastered it?
I asked Deeba Kazmi, Co-founder & Chief Data Scientist at FinbotsAI and India's Trailblazing Woman in AI, this exact question, someone who's built AI solutions for credit risk at scale and navigated Singapore's strictest AI compliance frameworks.
Her answer?
Watch on YouTube; listen on Apple Podcasts or Spotify.
Episode Summary:
In this engaging conversation, Aashka Patel interviews Deeba Kazmi, co-founder and chief data scientist at Finbots.ai, exploring the intersection of AI and FinTech. Deeba shares her journey from lead data scientist to co-founder, the mission of Finbots.ai in revolutionizing credit risk management, and the significant impact of women in leadership roles within the industry. The discussion also delves into compliance with regulations, the importance of explainability in AI models, bias monitoring, and the challenges of rolling out AI in legacy systems. Deeba emphasizes the need for high-quality data, the role of human oversight in high-risk scenarios, and the emerging trends in fraud detection. The conversation concludes with insights on AI literacy, the skills needed for the future of FinTech, and the relationship between AI and universal basic income.
Timestamps:
00:00 AIR Bites (Precap)
02:21 AI in FinTech – Everything You Need to Know
04:41 Deeba Kazmi & Finbots.ai – Founder Journey
07:27 Women in AI – Leading the Change
10:18 AI Compliance – FinTech Essentials
13:08 Explainable AI – Why It Matters
16:01 Data Quality for Better AI Models
18:54 Fighting AI Bias – Monitoring & Fixes
21:58 Upgrading Legacy Systems with AI
24:43 Human vs AI – Who Makes Financial Decisions?
28:23 How AI is Evolving FinTech Decisions
29:37 AI Guardrails – Keeping Finance Safe
31:41 Testing AI – Navigating Regulations
31:51 AI & Fraud – New Threats to Lenders
33:29 Credit Assessment – Insights from Emerging Markets
35:05 AI Literacy – What FinTech Teams Must Know
38:14 FinTech Future Skills – What to Learn
41:59 AI & Universal Basic Income – Financial Inclusion
45:01 FinTech Founder Tips – Thriving in Change
50:31 Outro
Transcript:
Aashka Patel (00:04)
Hello and welcome to On AIR with Aashka. Today we are diving into the world of AI and FinTech with one of India’s trailblazing women in AI. Yes, I am joined by Deeba Kazmi, co-founder and chief data scientist at Finbots.ai. She has spent over a decade at powerhouses like Citi, Morgan Stanley, Experian. So welcome to the show and thank you so much for joining.
Deeba (00:28)
Thank
you, Aashka. Thank you for inviting.
Aashka Patel (00:31)
Yeah. Yes. Let’s dive right into the questions. So you have been recognized as one of India’s trailblazing women in AI. Huge congratulations on that. So can you shed some light on your work at finbots.ai that brought you this recognition? And what do you do?
Deeba (00:51)
So at FinBots I am leading the product development enhancement aspect. So when I joined four years back, I joined as a lead data scientist and my journey from lead data scientist to co-founder has been over a period of two years. In these two years, I started working on the development of the product from scratch.
Aashka Patel (01:00)
Mm-hmm.
Deeba (01:20)
Not just that, but I was very deeply engaged in the pre-sale conversation, understanding what are the client requirements so that I can build that into the product. So that our product is not just a flashy product, which has AI built in it, but it actually solves real client problems.
Aashka Patel (01:42)
Yeah. Can you give us a little intro about the product? Like what space is it for the audience?
Deeba (01:49)
So FinBots was actually launched with the mission of developing AI products to solve the challenges that are there in the credit risk area of the banking industry. Now credit is a very important aspect when it comes to banking because it’s the money making part of the business. So although there are other banking products, but the real money really comes from credit.
Aashka Patel (02:02)
Good one.
Deeba (02:15)
All at the same time, there is a lot of risk involved in it. So, yeah, so because of the trade off between the money that it brings in and the risk that’s involved in it, there is a lot of need of coming up with products and AI solutions that help you solve this trade off. So, and this can be done by leveraging the lead data that’s out there and using the
Aashka Patel (02:20)
Yeah.
Okay.
Yeah.
Good
Deeba (02:44)
state
Aashka Patel (02:44)
night.
Deeba (02:44)
of the art AI algorithms and processes to make use of those data sources in the most appropriate manner, which can then lead to low risk credit lending.
Aashka Patel (02:55)
Mm-hmm.
got it. Got it. Yeah, that sounds very exciting. And of course, as you mentioned, it’s a high risk area, also classified by the EU AI Act. so I had this question about beyond the headlines, since you have spent over a decade in the FinTech industry itself, not just in AI. So what’s one
Deeba (03:11)
Yeah.
Aashka Patel (03:22)
concrete way, having more women in fintech AI leadership has changed actual business outcomes or product decisions at companies that you have seen or worked with.
Deeba (03:34)
Yeah, absolutely. There have been real and measurable impact on business outcomes, whether it be growth, whether it be innovation, ethics, team performance, and lot of such areas, which has led to a remarkable shift in the way the AI products have been designed. So now if you see the AI products are much more user-centric, inclusive, and effective. And a lot of...
Aashka Patel (03:42)
Thank
Mm, yeah.
Deeba (04:02)
This has to do with the inclusion of women in the leadership because they have brought in the aspects of the different data parameters to be considered, looking at the product from a human aspect, a lot of collaboration across teams, which has led to developing much more inclusive products, which are more effective, negotiation of adoption of AI.
Aashka Patel (04:08)
women leaders.
the day.
Mm.
Deeba (04:32)
So there is always a huge gap between the business team and the innovation team. So there has been a good amount of contribution by the women leadership in this negotiation and bringing them on board. Yeah.
Aashka Patel (04:32)
Mmm.
Yeah.
Yeah, yeah, yeah. That’s very powerful to hear and I totally agree with you and we need more women representation in the AI world as well. So let’s go back to the Finbots conversation and talk about some Singaporean regulations.
On the Finbots website, it’s mentioned that Finbots has completed MAS veritas and AI verify frameworks launched by the Singapore finance and technology regulators to objectively validate the quality and performance of AI solutions. So can you explain what they are and how did Finbots comply with them?
Deeba (05:27)
Okay, so it was a good two to three months of pilot that we worked upon in collaboration with MAS and IMDA. So these are the two government bodies of Singapore, which had a lot of technical tests on the product itself and the AI models that are being generated by the product, as well as process checks.
Aashka Patel (05:34)
Mm-hmm.
And.
Mm-hmm.
here.
Deeba (05:52)
So a huge list of questionnaires where you have to provide answers on the process that has been adopted to make the AI solution and the models that are being developed completely compliant with the regulatory policies that are out there. So as mentioned, a combination of technical as well as process checks, which includes all your major pillars of...
Aashka Patel (05:53)
Mmm.
in.
in.
Deeba (06:19)
your feet principle, which is fairness, explainability, accountability, and transparency. So it was much detailed process to really analyze the product. So if I go into the detail, the technical test really talks, gives you results upon what kind of AI models are being generated in terms of accuracy.
Aashka Patel (06:27)
annoying.
good.
Deeba (06:44)
in terms of fairness. So there are fairness parameters on whether the model is biased with respect to any of the gender class or age groups or ethnicity, explainability, whether it’s able to explain and come up with the features that are contributing to the result and are they accurate or not, whether we are using the approved algorithms and the latest algorithms for the different steps that are involved in the modeling lifecycle.
Aashka Patel (06:51)
and move.
Deeba (07:13)
what we are doing to maintain the model repository, keep track of the versioning of the models, keeping track of the models even after they are developed, validated, and pushed to production. So end-to-end check and assessment was done, and after that only we were provided that certification by these government bodies.
Aashka Patel (07:33)
Mm.
Yeah, yeah. So does it happen? Like, what’s the frequency of the check or the audit that happens? Like, does it happen yearly or
Deeba (07:45)
So no, so it happens once unless until your product undergoes a drastic change. For one AI product, it will happen once end to end.
Aashka Patel (07:48)
good.
a major change or something.
Deeba (07:57)
the process checks happens once when you have launched the product and it’s ready. And if it undergoes some bit of huge transformation, then you will have to go through the process again. the technical tests are something that can be infused within the solution. And every time you’re developing a model, if the client needs a certification, can.
Aashka Patel (08:10)
again.
Mm-hmm.
Deeba (08:23)
we can go through it really quickly because there’s a tool kit and we can provide it, just provide it to them on the go.
Aashka Patel (08:26)
Okay, yeah.
Yeah, that’s actually quick and very smart way to approach it. Yeah, because with I think SOC two compliance or something you need to get it done like every now and then type one. Yeah,
Deeba (08:34)
Exactly.
Yes, yes, yes, yes. Those are the, those
certifications we get on a yearly basis, the SOC two type and engineering certification.
Aashka Patel (08:52)
Yes. Yeah,
yeah, that’s interesting to know. So you mentioned about the explainability part. Let’s dive right on to that. So the EU AI Act also requires clear and meaningful information about automated decisions. But here’s the paradox. When you give customers detailed LIME or SHAP explanations, some become very suspicious or even litigious, especially seeing factors they
Deeba (09:14)
Yeah.
Aashka Patel (09:20)
considered irrelevant. So there’s also a technical issue like LIME creates simplified approximations that can sometimes be misleading. So it might highlight income as the top factor when your model is actually weighing complex interactions between income, spending patterns, regional indicators, like all of that. So how do you basically overcome these kind of challenges while
Deeba (09:21)
Hmm.
Aashka Patel (09:47)
infusing explainability within your solutions.
Deeba (09:50)
Yeah, so that’s a very important question. So the USP of the product is that it has automated all the model development cycle. So although there is human oversight and control, but you can run all of this very quickly at a click of a button, along with having control. So you can provide your inputs as well. How it helps is once you develop your model, at the same time, you get a very well
Aashka Patel (10:04)
Okay.
Boom.
Soken.
Deeba (10:19)
automatically documentation available, which gives you all the details about the model, the overall development process, the top variables, the contribution of each of the variables. And then this model, because it’s a credit risk space and it’s not any other space, it’s a credit high risk models. So it cannot go to production without getting an approval from the regulators or the model risk management team of the organization.
Aashka Patel (10:24)
Go for it.
Yes.
in the room.
Deeba (10:49)
modelers can straight away pass on this auto-generated document to them and let them go through the top contributors. And if they have issues with certain model variables, they can just mark them out. And over here, the modelers can just quickly at a click of a button, rerun the model with the updated set of variables and share that with them. before going to production, the...
the model risk management team has to approve those variables, which happens really quickly through an automated fashion through the documentation that’s auto-generated. That’s one. So in no point that the regulators will come to know, why this variable is being used or why that variable is being used, it has to go through that approval process. Second is, this is global explainability when we are developing the model.
Aashka Patel (11:17)
Good.
Really.
You know.
Yeah
Deeba (11:43)
now talking about the local explainability when the model is in production and the explainability is provided at a customer by customer level. At that time, also given the risk involved, not everything goes through an automated yes or no or decline or accept kind of scenario. We do understand that at times there is human oversight involved.
Aashka Patel (11:47)
No.
Yeah.
Okay.
Deeba (12:12)
And so wherever we feel that there is a risky region, and let’s say the top percentile of the customer base model is very good in identifying and giving the top parameters. But let’s say there are segments where there might be some scope of misalignment between a human inspection and the AI inspection. we, again, through an automated fashion, it will go through a manual review.
Aashka Patel (12:13)
Mm.
room.
Yeah.
Deeba (12:42)
So where the decision maker can go through it and then pass it on, if everything looks fine to him, then he can pass it on to the next stage.
Aashka Patel (12:43)
Okay.
Good one.
That sounds very interesting. you mentioned about the local thing, localized explainability. does cultural relativism play a huge role when you are defining? Because there is a human in the loop or human oversight is there, so it’s tuned accordingly.
Deeba (12:58)
local. Yeah.
No, it depends upon the values that that particular customer has for the different data points in the model. So the local explainability again comes from the AI algorithm only. So it will assess, yeah, it will assess maybe at a global level and for majority of the people, income is playing a big role in his application getting accepted or declined.
Aashka Patel (13:22)
Mm-hmm.
Only, okay.
Mm-hmm.
No.
Deeba (13:39)
But for this particular customer, it might be something else. Maybe it’s his income to expense ratio or loan to value ratios for his past credit score. So it can be totally different because the values that this customer has and the interaction that’s happening between the variables for this particular customer might be very different. So that’s why local explainability plays a key role for decision making.
Aashka Patel (13:40)
Mmm.
Boom.
Boom.
No,
Yeah.
Yeah, So explainability isn’t just like, our models are explainable or explainable AI is implemented. It’s much deeper than what we see on the surface level. So yeah, it’s very interesting to know that and localized is very like mind blowing to me at least. Yeah, yeah.
Deeba (14:20)
Exactly.
Aashka Patel (14:30)
So within your product, you have this model builders right? Within the creditX product. So EU AI Act requires high quality data sets for forecasting and risk models. As we mentioned, it’s a high risk scenario. So since you are letting the clients build their own models from their own data, so how do you maintain this kind of...
Deeba (14:47)
Mm-hmm.
Mm-hmm.
Aashka Patel (14:56)
help maintain this kind of high quality data and integrity on your platform.
Deeba (15:01)
Okay, so there are two ways we do it. Stage one and stage two. Stage one is when we are deploying our solution in the client’s environment, we will definitely do a initial level of analysis to see, to check the quality of data and to understand what’s the appropriate usage of data. It’s not a...
Aashka Patel (15:05)
No. No.
Okay.
Deeba (15:23)
every time and for every data that we would recommend that you go ahead and develop an AI model. Maybe that client has just started lending and they don’t have enough data points, or they don’t have a mature repayment history. So whatever, or they still need to procure information from third party sources, which are still not there.
Aashka Patel (15:26)
Yeah.
Mm.
Hmm. Hmm.
Mm-hmm.
What?
Deeba (15:47)
So depending on
any other reason, so depending upon the quality of data they have, we will make recommendations around what type of model they should be developing, whether they should be going with an expert scorecard, what we call as an expert scorecard, which is just based on business judgments, because there is no data to train. So if there is zero data, so there is no training data available for the AI algorithms to learn the patterns, right?
Aashka Patel (16:02)
Mmm.
Yeah.
Deeba (16:17)
recommend them to go ahead with an expert model, which can be implemented, created and implemented through a product called DecisionX, which is a business rules engine. through simple rules, you can have an expert judgment-based model. So that’s stage one. And if there is good enough data, then you go ahead and develop an AI model. Again, we take a call between going ahead using a traditional
Aashka Patel (16:17)
Mm.
Okay.
Yeah.
Yeah.
Deeba (16:46)
approach or an advanced approach again, depending upon the data quality. So those recommendations is something that we provide. Stage two is built in the product. So we have a specific data curation pipeline which takes care of all the issues in the data, whether it be missing value, anomalies in the data, fluctuations in certain distributions.
Aashka Patel (16:59)
Good.
Yeah.
Deeba (17:11)
bias in the data, imbalanced data. So all of those which can be curated through algorithms are built in the product to take care, to maintain the quality.
Aashka Patel (17:18)
in.
Yeah,
does your product also gives the ability to create those model cards that are required by many regulations like the model cards or the system cards that we see with OpenAI models or
other AI models. So do you also help them create those kind of documentation Got it, got it. Yeah.
Deeba (17:49)
Yes, yes. All the required documentation
are generated automatically and it gets downloaded at a click of a button.
Aashka Patel (17:55)
Mm-hmm.
Okay, got it. So the one thing that I loved while I was researching for this podcast was the AI toggle feature. it’s very interesting. Since I’ve been a product manager, and I’ve seen that people are, overusing AI or maybe like applying AI to problems where AI is not even needed. Traditional ML can do the work for us.
Deeba (18:22)
I that’s
Aashka Patel (18:24)
So that’s an amazing feature to have. yeah. Yeah.
Deeba (18:27)
Absolutely. Yeah, absolutely. That’s very much required because
one should make a decision to whether to use AI or use a traditional approach, looking at some stats, Not just go ahead and if I feel like I go ahead, use it. So that’s more intentionally included in the product. So people who are AI ready, they can go ahead and start using the advanced one. Who are starting new, they don’t want to take that jump.
Aashka Patel (18:37)
Thank you. Mm-hmm. Mm-hmm. Yes.
Yeah.
English.
Good
Deeba (18:57)
Start with a traditional approach, check out the results they are getting using AI. if they see a drastic improvement, then they can make their own choices on the basis of some stats rather than human
Aashka Patel (19:00)
Yeah.
Yeah, yes, yes, yes. That’s very powerful. So since we touched upon bias a little bit in your answers, so let’s talk about bias monitoring. So you have talked about identifying and eradicating biases. So can you share a specific instance where your post-deployment monitoring caught the model that had drifted and become less fair over time? And what
demographic got hurt and how did you recover it and what was your 48 hour response plan?
Deeba (19:43)
Yeah, so Aashka in today’s day and age where our environment is so dynamic, population shift in your model is eventually going to happen. And unlike earlier days when the model used to be stable for a year, two year, it’s not happening these days. And if it is happening, there’s something definitely wrong because there are changes happening.
Aashka Patel (19:52)
Yeah.
Happen,
You’re wrong.
Deeba (20:12)
Either you’re not monitoring it carefully or wrongly monitoring it. So what we have in place is automated monitoring reports. So every week these reports get generated. Usually what the reports capture is, I’m talking about the regular approach, they only capture the performance of the model and if it’s doing good, they’re okay. However, your model can still be doing good, but there can be a population drift, right?
Aashka Patel (20:17)
Yeah.
Boring.
Really.
Deeba (20:41)
like they are able to capture bad from goods, but the cutoff at which you started defining your bad versus goods might now need to change because there has been a drift in the population. Now this drift can be based on either gender. So maybe now more females, we are seeing so many females more actively applying for credits these days. Earlier it was to be only the males of the house.
Aashka Patel (20:41)
Yeah.
Mmm.
Yes.
Deeba (21:07)
that were going to the banks and doing those activities. So that’s there. Then age also, a lot of younger people. Earlier we used to see only mid-aged people applying for credit Now the youngest people are also getting jobs and they are applying and these trends are shifting from time to time. So what we have captured in our reports are these bias aspects as well.
Aashka Patel (21:08)
Yeah.
Yeah.
Yeah.
Deeba (21:37)
how well the model is doing across different gender segments, age groups, ethnicity, and if something goes haywire, it will send alert to the credit modeler and he can go back and refresh the model very quickly on the most recent data and then push the model to production. So that can happen within a period of one or two days that you can take that action and stop any kind of...
Aashka Patel (21:47)
Thank
Boom.
Yeah.
Deeba (22:06)
wrong decisions being made.
Aashka Patel (22:09)
Yeah, that’s a very good incident response plan also, Like having that quickness and everything. So since you mentioned about this, you with some customers you
try the non-AI or the traditional ML approach first and then based on the stats then you help them get into the AI component of things. So you must be working with some banks who have been using legacy systems like hardware, infrastructure and everything.
So what have you found works best for rolling out AI in environments with old infrastructure, especially in banks across emerging markets because you have been working with emerging markets a lot. So any lessons from that?
Deeba (22:56)
So the good thing was that we did this research earlier before completing the development of our product so that we can take care of all the reluctance or hurdles that are there from the flying side in terms of IT and in infra and tech So that was one important research that we did at the very beginning. And that’s where we decided that we are going to develop a lightweight containerized solution.
which can be deployed in any environment. So there are no restrictions that this needs to be deployed only on cloud or our public cloud. No, the product can be deployed on-prem, private cloud, or on our cloud. But we do mention the benefits of deploying any solution on cloud versus on-prem so that later on, if they want to make that shift, they can do it with our help.
Aashka Patel (23:29)
Mmm.
Green.
That’s a good transformation that they can have. We have talked about human oversight a lot during this conversation.
I have an odd analogy. Like when we shop at a supermarket, there’s often someone at the exit checking your bill against your cart. Like sometimes they verify every single item. Other times they just scan one or two items at random. if you have an unbilled item, you would definitely get caught with the thorough check, but you might slip in just with a spot check.
So in like how much amount of human oversight is enough would you like someone to be manually reviewing every decision or does random checking work in this high risk scenario?
Deeba (24:39)
Okay, so as you rightly mentioned, it’s a high risk scenario. So we have to be very careful where we are allowing automated decisions and where there is a need of human evaluation. So there are segments at the top and the bottom the size that the model is extremely confident and we know the level of confidence that the model has. All those stats are available. So we put cutoff so that
Aashka Patel (24:50)
Okay.
Yeah.
Deeba (25:06)
anything passing through these regions can just go straight through. Whatever decision is being made by the model, just go straight through. But wherever we know there are risky segments, and within the risky segment, there is also high, medium, low. We channel them through human evaluation, but different degrees of human evaluation. There can be where the model is very less confident about the decision, then
Aashka Patel (25:11)
Okay.
Good night.
Deeba (25:34)
it will go through a longer process of human evaluation, maybe asking the customer for more documents or something. But if it is less risky or edge cases, we would, the human evaluation would be just review the reason codes if it looks fine, then pass through. So those kind of categorization in terms of how the output should be rolled through is taken care of within the solution. It’s just that the cutoffs
Aashka Patel (25:41)
Hmm.
Boom.
Okay.
No.
Deeba (26:02)
can be adjusted accordingly for different models.
Aashka Patel (26:04)
my.
Got it, got it. But this cutoff that you are talking about might have evolved over a time, right? Like when you just started with Finbots or this product, like was that the case from the very beginning or did any incidents happen?
Deeba (26:19)
No, Yeah,
earlier we started with binary decisioning, which was accept, reject. But then of course, clients were quite reluctant about we cannot just do state, say true for everyone. We have to be able to get deeper into the kind of the quality of output that’s being generated in terms of the confidence. And then we segmented further for the type of action to be taken on
Aashka Patel (26:26)
Yeah, okay.
Yeah.
Yeah.
green.
Got it, got it, makes sense, makes sense completely. since we talked about this human oversight thing, the world is moving towards more and more autonomous AI. And recently there have been reports around AI agents executing unauthorized financial transactions despite safety guardrails.
in your Finbot scenario, it’s credit scoring or credit rating. So a similar vulnerability could mean wrongful loan approvals or denials affecting thousands. So what specific guardrails or safeguards have you implemented that you believe most FinTech AI systems are missing these days
Deeba (27:16)
Yes.
the first thing I feel is in a high risk area, people shouldn’t rush through the AI wave that’s happening. I understand to adapt something for marketing, for campaign offers, that’s absolutely fine. But if something as important as credit risk, with their one decision,
Aashka Patel (27:35)
Yeah.
Good morning.
Mm.
Deeba (27:47)
And just think about very high ticket size loan, where one decision, wrong decision can lead to huge, huge, huge loss. So the guardrails there has to be very, very well thought upon, researched, tested, evaluated over a period of time. And it has to be rolled out in a phased approach. So that’s.
Aashka Patel (27:57)
Yes.
Bye.
Yeah.
Deeba (28:14)
very important rather than just developing something and really feeling happy about it and sending it across. And also it should be used in patches. For example, there is an overall modeling life cycle and decision making process. So not in one go you are including everything in one go where testing and evaluation and tracing back the issues become a huge challenge.
Aashka Patel (28:20)
Yeah. Yeah.
Mmm.
replacing everything.
Deeba (28:42)
You doing it in patches. We are also doing, but we are not replacing the entire thing by LLM or, or AI agent. We are doing it in patches, for example, for data. So we have brought in the VLM which is visual language models to transform and let’s say image data or scan copy, digital copies, and then to extract information from all of that, to use it for decision. Yeah. So it is, it should happen, but yeah.
Aashka Patel (28:43)
and good night.
Thank you.
No.
Thank you.
Deeba (29:12)
it happened in patches where you can very well validate it.
Aashka Patel (29:18)
Yes, yes, makes a lot of sense.
so what’s the most sophisticated new fraud vector that you have encountered in the last six months that specifically targets AI powered lending systems?
And how are fraudsters using tools like deepfakes or synthetic identities and what’s your counter-strike against them?
Deeba (29:37)
Yes, deep fakes and identity fraud. See, identity frauds have been happening for a very long time, but there was no solution for it. But now with, of course, the new AI out there, it’s a boon and a pain. So the pain is there will be more frauds and new types of frauds. But yeah.
Aashka Patel (29:45)
Yeah.
Thank you.
frauds.
Deeba (29:58)
So
a deepfake might not be, it might be difficult for a human eye to identify deepfake, but the AI again is much more capable of identifying deepfakes. So the need is just to keep updating your AI algorithms to identify fraudsters to include all these aspects which can then be taken care of.
Aashka Patel (30:04)
Yeah.
Thank
Any recent incident that has occurred that you would like to share? Beware of such.
Deeba (30:28)
No, think people, bands are
much more vigilant. they will introduce us. They ask us to introduce these things much before they encounter anything like.
Aashka Patel (30:34)
Yeah.
Yeah
Yeah, yeah, makes sense. Yeah, so you have worked across India, Singapore and other emerging markets. So can you share one specific insight from African or Southeast Asian fintech that could revolutionize how Western banks think about credit decisions like something that they have been missing and the South region is doing much well, Southeast region.
Deeba (31:08)
I think the scale at which the banks and the fintechs really work in this part of the region is immense. that scalability is absolutely missing in that part of the world and diversity in the data being used. Of course, here there is a need because they don’t have formal
Aashka Patel (31:11)
Mm-hmm.
in the room.
and
Deeba (31:30)
credit bureaus and data repositories to use. But because of that, people here have become very creative in the sense of what are the different data sources they can tap into and use it for their credit risk assessment for other purposes as well as the marketing, digitization or any other space. So a lot of work is being done in the space of using alternate data source.
Aashka Patel (31:42)
Mmm.
Deeba (31:58)
which we hear a lot in that part of the region, but to be honest, it’s not being implemented that well as it is being implemented here because of course the need over here is much more digital wallet data, transactions data, your telco data, your website data, digital footprints.
Aashka Patel (31:59)
Mmm.
Mm-hmm.
nice, that’s interesting,
when
Deeba (32:22)
We cannot even, like five years back, couldn’t have thought about Southeast Asia tapping into these data sources and using it. this is reality. This is what we are doing. And they are doing it at scale.
Aashka Patel (32:29)
Yeah.
Yeah, that’s very important. Yeah, so let’s move on to AI literacy. AI literacy is also a huge part of the EU AI Act.
Deeba (32:44)
Thank
Aashka Patel (32:45)
for non AI companies, it’s like legal services require advanced proficiency while marketing departments need only basic level awareness and everything. But for AI companies, what does it mean to be AI literate?
Deeba (33:00)
Being an AI company, we are really responsible of the knowledge that we have and the knowledge we are spreading. So if we call ourselves the AI company, we have to be up out there and have the latest information available and also assess it well. So if I’m going to talk about something, I should know what are the pros and cons. I cannot just read two articles and give a speech on a new algorithm.
Aashka Patel (33:08)
and then.
Amen.
can you?
Deeba (33:25)
We always,
what we have is a small research team. So whatever new comes up before talking about it or even making a decision around implementing it or including it in our solution, we have a research team where we run a couple of experiments to get deeper into it and to understand what it is, what actually it is and what is being talked about. So that’s our knowledge base.
Aashka Patel (33:50)
Yeah.
Morning.
Deeba (33:56)
First responsibilities with every new thing coming in, create a right, trustworthy knowledge base. And that’s the AI team. That’s the responsibility of the AI team within the AI company. But not everyone is part of that. But then once we have that information, we make sure that we spread it across. Of course, there are monthly meetings where we introduce it to the other team members also. And the very important part about being part of FinBot is
Aashka Patel (34:11)
yeah.
Amen.
Deeba (34:25)
even people, whether it be marketing, whether it be engineering, whether it be finance, everyone knows the product inside out. If you go and talk to anyone about the product, they will be able to. Although the AI product is very technical, but that’s the basic goal that we have set up for everyone. So you cannot keep working for an AI company and say, I’m from marketing. I don’t know anything about it.
Aashka Patel (34:34)
Yeah.
no.
Yeah.
Yeah, yeah, yeah, that is actually very powerful. Like, I have worked with a company where the salespeople used to come back with questions on security, privacy And they are just knowing how to sell the product and what the product is like just on the outside or the surface level, but they don’t know the nitty gritties, the safety, privacy, security nitty gritties.
Deeba (35:02)
you
you
them.
Aashka Patel (35:11)
So I have seen that in the corporate That’s very powerful literally great work. when we talk about this, Anthropic’s economic index shows AI use leads more towards augmentation, like 57 % as compared to automation, that is 43%. So it also reveals like usage.
Deeba (35:12)
you
Aashka Patel (35:37)
peaks in limit to high wage occupations like computer programmers and data scientists. So from your economics background and hands on fintech experience, how is this reshaping the skills that fintech companies need and who is getting left behind in this scenario?
Deeba (35:53)
See, whoever is thinking that this is not going to go too far, they will be left behind. So I think the problem first to tackle is the mindset rather than the skill set. So having the right mindset and going with the time is very much important. And that’s why adapting to what is coming in new. So like computers came in, internet came in, then...
Aashka Patel (36:00)
Yeah.
I mean
Deeba (36:20)
traditional modeling came in. So with everything coming in, we always thought that this transformation will lead to job losses, job losses, job losses. It will happen if you keep sticking to your current skill set. it’s always very important to adapt, whether it be data scientists, now from coding, they have to move to where should this app apply. So it’s not like the opportunities are going this.
Aashka Patel (36:29)
Yeah.
Bye.
Deeba (36:48)
new technology, new algorithms are going to open new opportunities. So opportunities are there. It’s just that you have to have the right mindset and transform your skill set to tap into those opportunities. Then your jobs won’t be, your jobs will be secured.
Aashka Patel (36:50)
in one.
Good night.
Yeah.
affected,
replaced. Got it, got it. So can you point, of course, like can you point some specific skills that FinTech will be demanding in the future that people can develop alongside the actual hardcore skills that are required, but like any other skills that you can point out?
Deeba (37:28)
Yeah, so prompt engineering is very, very, very important. Now, when I say prompt engineering, it’s not just a prompt engineering that we do to plan a vacation or something like that, but for our Inbuilt solution. So how you’re asking, what you’re asking, it’s exactly like when you have a big business problem to solve for, you always break it into tasks.
Aashka Patel (37:35)
Mm-hmm.
Yeah, very complex level.
Amen.
me
Deeba (37:56)
and or smaller
Aashka Patel (37:57)
the best.
Deeba (37:57)
problems depending upon what it is. And then you assign a different process to solve for it. So prompt engineering has become that important. So that’s one. And second space where you have to, anyone has to become more developed and have great skill set is on the validation and guardrail.
Aashka Patel (37:59)
Mm-hmm. Mm-hmm.
important.
Deeba (38:22)
You can keep developing things, but you have to come up with a skill set on what type of guardrails should be there. It should be now that a guardrails AI based human in the loop. Again, another layer of LLMs, multiple layers of LLMs. There multiple ways of doing it, but when what should be adapted, how to set up the entire process for it. So a lot of.
Aashka Patel (38:24)
Mmm.
No.
No.
Deeba (38:48)
thinking
through, thinking from solving a problem kind of mindset needs to be there. And of course, how to set up that overall architecture, so architecture design. are coding and coding and all is good, but then those skillset will become less important and these skillsets will become much more important.
Aashka Patel (38:55)
my
Yeah.
Thank you.
Yeah.
Yeah, yeah, I agree to your point. like, of course, if the employees know prompt engineering well, then it will cost less to the company as well. So they can write very contextual and precise prompts and get the output that is needed. So one last and deep question. Sorry.
Deeba (39:22)
Every hit and miss is a cost to the company.
Aashka Patel (39:35)
Yeah, exactly, exactly. Yeah, yeah. So last question. The UBI discussion is going on, right? The universal basic income.
So studies have predicted AI will displace about one third of existing jobs worldwide within a decade and universal basic income is gaining momentum as a policy response. So this creates a paradox for financial services, especially with your AI credit inclusion tools, like they expand financial access right? But the same technology might eliminate the jobs that create income streams to service those loans. So from your economics background,
How do you like, or do you see AI financial inclusion and UBI as complimentary or competing approaches? And practically how would you design credit models for a world where a significant portion of borrowers might receive guaranteed government income rather than traditional employment? how would this world look like? Paint us a picture.
Deeba (40:34)
I think it’s a transformation.
think the AI AI advancement and UBI are going to be complementary and will help this transformation to go peacefully rather than creating a lot of disruption in the society and in different economies. Talking about the need of credit, no, the need of credit is going to be same or increasing over time irrespective of the UBI coming in because
Aashka Patel (40:38)
Good night.
Mmm.
Mm-hmm.
Deeba (41:03)
Credit basically gives you more power, more purchasing power, while being able to repay your credit with the help of UBI that’s going to be provided. So it’s not going to impact financial inclusion. It’s not going to impact the credit industry. In fact, it will increase more demand for credit because there will be more products and services and other things that people would want to use at the same time they didn’t have the sorority.
Aashka Patel (41:08)
Power.
No.
Deeba (41:31)
of an income flow coming in through the UBI. But I feel all of this is going to be just transformational. After the transformation is over, we have gone through the other side. People will be settled in and they would have identified their respective space where they can contribute and start earning. Because there are things that will get disrupted. The jobs will go, but again, it will definitely create more opportunities, newer jobs.
Aashka Patel (41:36)
Mmm.
Bye.
Mm-hmm.
more.
yeah.
Create new, yeah.
Deeba (42:01)
for people to get employed into.
Aashka Patel (42:05)
Yeah, yeah, yeah. So it will be not a steady transformation that we will go through, but after that, the grass will be greener on the other side. Yeah, yeah. That’s very important. So one last piece of advice for FinTech founders, like.
Deeba (42:08)
New world
again.
you
Aashka Patel (42:24)
Firstly,
let’s start with the biggest mistake that you have seen them making. And secondly, what would be the one piece of advice that you would give to fintech founders trying to survive in these environment of tight regulations and aggressive regulations?
Deeba (42:39)
I, the first mistake I would say is thinking too much can be harmful because I think if you keep on thinking and planning and not really doing things, you will not know what’s working, what’s not working. So in the beginning, when we were developing the product, we were like, no, we will think through this perfectly. And then only we will start the implementation so that the development goes smoothly.
Aashka Patel (42:44)
Hey
Uh-huh.
Mm-hmm.
Bye.
Deeba (43:07)
So that’s one example, but that can happen with anything with any kind of AI adoption. By the time you, if you’re taking too long to think and plan, there will be 10 new things that would have, would have come up, could replace 10 things in your plan already. So now it’s the time to learn, to learn quickly, develop quickly, fail quickly. It’s okay to fail. You are bound to fail. If you don’t fail, also you’re doing something wrong.
Aashka Patel (43:13)
Good night.
Yeah.
in the name.
Mm. Mm. Mm.
Deeba (43:38)
to learn again and then get back to track of development. that’s the thing. I really realized that sitting back, thinking and planning, those were the old good days where we used a month or two, one to do the planning only. here, yeah. And the thing,
Aashka Patel (43:38)
It’s wrong, yeah.
Yeah.
Yeah, the waterfall model in a way. Yeah.
Deeba (44:04)
The learning for me, so this is the thing that I’ve changed about myself. The other thing that I have kept is, as mentioned, me keeping myself relevant and keeping myself updated and creating my own knowledge base to make my decisions. Because there is so much coming up every day and at times it gets over whelmed
Aashka Patel (44:05)
Mm-hmm.
And you’re.
Morning.
Mmm.
Deeba (44:30)
with the kind of information that’s going in, the kind of research and development that’s happening. So there’s always a requirement of a reality check. What’s true, what’s just in the air. So that is something that I’ve always done. And I really want to continue doing it.
Aashka Patel (44:32)
Yeah.
Mm, yeah, yeah, yeah, yeah.
Mm-hmm.
Yeah, yeah, that’s very powerful. are there any go-to sources that you like refer to? Because with AI, content generation has become huge and like referring to the right resources is also a hassle. Can you point any specific resources or people that you follow to get the latest updates or keep yourself updated?
Deeba (45:19)
Anything
that’s not supported with proper research in terms of a research paper with code and data, I don’t fall into that so easily and nobody should fall into it. So go for well-reputed research papers which are trustworthy.
Aashka Patel (45:24)
and
Amen.
yeah, we yes.
Even.
Deeba (45:44)
and are supported by codes and data, which you can very quickly check on your side as well. Articles and all, it’s good to read, but again, as you mentioned, the content in LinkedIn articles, don’t know how, not even one percent of the people are writing it themselves and you can make it out very easily. So I don’t think for those are...
Aashka Patel (45:44)
Mm-hmm.
anyone.
Okay.
Yeah.
Yeah.
Deeba (46:12)
the right sources of information to gather knowledge from But the re search papers are always my go-to place to get that trustworthy information.
Aashka Patel (46:14)
Come on.
.
Yeah, yeah, yeah. Like a proper data scientist kind of a response. just, yeah.
Deeba (46:32)
Yeah, and for people who don’t have a technical background, for
them, I think it’s difficult for them to really, they want to know what’s happening in AI. Look at articles where it’s been talked about the AI being actually implemented.
Aashka Patel (46:49)
No.
Deeba (46:56)
Yeah. So if they are talking about it and they have implemented and they’re talking about something they have implemented, it’s OK to trust that if it’s publicly available, try it out. But if it’s something in the development stage and they are saying that they will do this and that, it might be all in the air unless and until Because to be very honest, there are thousands and thousands of pilots that are being run today.
Aashka Patel (47:01)
Okay.
Mm-hmm. Mm-hmm.
Evening.
Thank you.
morning.
Yeah.
Deeba (47:24)
in the GenAI space with in-reputed consultancies and big tech firms. But if you talk about what’s in production, you will not get an answer. So if you really want to do a quality check, ask what’s in production and what results.
Aashka Patel (47:25)
You know, anyway.
Yeah.
Mm-hmm. Mm. Yeah. Yeah.
Yeah,
that’s a shrewd response and a shrewd way to kind of cut through the hype and make a judgment on your end. Like, okay, this is to be trusted and this is not to be trusted. yeah, completely makes sense.
Deeba (47:53)
you
Aashka Patel (48:00)
So thank you so much, Deeba Thank you so much for your time. And I think right on time we are completing this. Let me stop the recording.
Deeba (48:01)
you
Yes.
you
