Challenges in Implementing AI-Based Translation Solutions in BFSI and How to Overcome Them

If you sit with a banking team long enough, you'll notice something interesting. They track every number, drop-offs, repeat calls, unresolved tickets, but very few dashboards capture the simple problem of “the customer didn't understand the message.” Yet this is precisely what happens with Odia-speaking users. They're fine with the product. It's the wording that feels distant.

This typically occurs when someone in the room suggests, “Let's bring in AI translation.” And that's where the journey begins, full of optimism, followed by a string of tiny complications that weren't visible at the start.

Accuracy Sounds Simple, Until Legal Teams Read the Output

The first surprise hits early. AI tools can translate fast, but in BFSI, “fast” isn't the goal; precision is. Odia phrases carry tone differently, and one misplaced verb can change the intention of a financial instruction. No risk team wants to explain a compliance issue caused by a mistranslated sentence.

Most institutions end up creating a mini vocabulary bank: how to express interest rates, penalty wording, policy periods, and so on. Once the model learns these, the results of English to Odia translation become far more predictable. The legal team breathes easier, and the AI tool stops sounding like a literal dictionary.

Legacy Systems Push Back in Their Own Quiet Ways

Another issue shows up in the tech layer. Many banks and insurers rely on systems built long before multilingualism was considered significant. When you try to plug in translation engines, the system doesn't break, but it resists.

Teams that succeed usually start with one journey, not ten. Something simple: onboarding steps, EMI alerts, renewal reminders. Once that works, they extend the setup. Slow, steady, boring, but effective.

Data Security: The Fear That Delays Everything

Every BFSI professional knows how carefully customer information must be handled. So the moment an external tool enters the conversation, the question becomes: “Where does the data go?”

The workaround isn't complicated. Sensitive fields are masked, or the translation occurs within the institution's own environment. In many cases, only the instruction text needs translating, not the personal details inside a document. Once teams realise this, security concerns shrink to a manageable size.

Tone Is a Bigger Issue Than Translation

Literal translations often sound stiff. Odia has a warmth and rhythm that doesn't always survive when content is converted mechanically. Customers feel it instantly, even if they don't articulate it.

BFSI teams that improve quickly usually pay attention to honest customer conversations. Support calls, branch interactions, and chat messages reveal how people naturally phrase doubts. When this tone feeds the translation engine, the output stops sounding robotic and starts sounding familiar.

Internal Adoption Has Its Own Learning Curve

Even when the translation tool works, teams don't automatically know how to use it. Some departments save old versions, others rewrite things on their own, and soon you have five Odia variations of the same message floating around.

A small set of ready templates solves most of this. Once teams see how these templates reduce confusion, adoption happens naturally.

The consistency issue becomes clearer over time.

As more content gets translated, small variations start to surface. One team might use “repayment window,” while another prefers “repayment period.” Both are correct, but customers still notice the inconsistency.

Most BFSI institutions eventually set up a central content store. Every piece of approved English to Odia translation goes there. When the English text changes, the Odia version gets updated. It's a quiet process, but it prevents a lot of confusion.

Many BFSI teams expect AI translation to slip neatly into their workflows, but the reality is far more uneven. Financial language doesn’t behave like regular content. When you run strict instructions or policy notes through English to Odia translation, the meaning sometimes shifts in ways that only show up later in customer queries. Older systems add their own resistance, and security checks slow things down even more. Still, when teams experiment in small steps—fixing the most confusing messages first, adjusting tone based on real customer conversations, and tightening a few key terms—the whole experience gradually becomes clearer and far more usable.

Where This All Leads

Despite the hurdles, the goal is simple: more transparent communication for customers who prefer Odia. AI translation won't solve everything instantly, but with patient adjustments, it removes the friction that used to feel unavoidable.

And once those friction points disappear, something else happens. Drop-offs shrink, complaints become more pointed rather than confused, and customers feel the institution is speaking to them, not at them. In financial services, that sense of clarity is often what keeps people loyal.