GTech climbed from 7th to 5th in the software and IT sector in the Turkishtime AR-GE 500 research, which ranks the companies with the highest R&D spending in Turkey.

AR-GE 500 is compiled from companies’ R&D expenditures, R&D headcount, patents obtained in their R&D centres, utility models, design registrations and trademarks, and is regarded as one of the most comprehensive references for Turkey’s innovation ecosystem.

With more than 26 years in banking and financial technologies, GTech continues to invest consistently in R&D to advance its Symphony product family and analytics solutions.

The post Our R&D Growth Continues: GTech Ranks 5th Among Turkey’s Top R&D Investors in Software and IT appeared first on GTech.

The awards, presented in the program’s 23rd year, were announced on 12 August 2026. Winners will gather at the ceremony in Paris on 28 October 2026.

Gold Stevie — API Management Solution Category

“Symphony Labs, the World’s First Service Orchestration Platform with a Generative AI Architecture”. Symphony Labs embeds generative AI capabilities into its architecture and manages complex integration and service orchestration processes from a single platform, accelerating the connection of existing systems with new digital services.

Silver Stevie — AI for Customer Experience Category

“Inflight Waste and Catering Optimization”. A project that makes inflight catering management more efficient through AI-based forecasting methods, improving the passenger experience while reducing waste.

Silver Stevie — Achievement in Financial Services Category

“A Digital Solution That Simplifies Complex Processes in Vehicle Financing”. Developed within the Symphony Lending product family, the solution digitalizes end-to-end consumer financing and supports decision mechanisms with AI capabilities.

Bronze Stevie — Achievement in Product Innovation Category

“Symphony Banking: From Legacy Systems to Autonomous Banking”.

Mine Taşkaya, Founding Partner and CEO of GTech, said: “With more than 26 years of experience, we witness the development of the sector and we are happy to contribute to it.”

The post GTech Wins Four Stevie Awards at the 2026 International Business Awards appeared first on GTech.

We were recognized by Bilişim 500, the definitive benchmark and most authoritative index of the Turkish IT industry, for our strength in fintech and software development, in two categories:

• Sectoral Software — Finance & Banking: #9
• Turkey-Based Software Developer / Producer: #25

Bilişim 500 ranks Turkey’s top five hundred IT companies each year by criteria such as revenue, exports, employment and product revenues.

This ranking, achieved with more than 26 years of experience in banking and financial technologies, reflects the trust placed in our Symphony product family and analytics solutions.

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Next Best Action (NBA) is a decisioning approach that picks, in real time, the single most valuable move to make for each customer right now. Present a loan offer? Send an alert? Do nothing at all? NBA answers that with learning models, not static rule tables. And it’s no accident it became one of banking’s most-discussed analytics topics in 2026. Here’s the telling part: in one industry study, NBA came out as the single most-wanted AI capability among CDP buyers, ahead of content generation and even segmentation.

This piece covers what NBA is, where it parts ways with classic campaign management, and the decisioning architecture underneath it.

What Is Next Best Action?

NBA is a strategy that fuses customer data, business rules and AI to determine the most fitting action for each individual. The “action” doesn’t have to be a sale. Reminding someone about an investment opportunity, suggesting they review a card limit, or making no offer at all are all valid actions.

That’s exactly where it splits from traditional campaign management. The old way: define a segment, blast everyone in it the same offer, measure weeks later. NBA cares about the individual instead of the segment, and the moment instead of the calendar. The second a customer opens the app, it fires whatever action matters most in that context.

An example makes it concrete. Recommending a card with foreign-spend perks to a frequent traveler makes sense. But if that same customer has called support three times in three months, the right next action isn’t an offer, it’s routing them to a fix. NBA creates value to the degree it can tell those two apart. If it can’t, it’s just a faster spam engine.

How Does NBA Differ From a Classic Recommender?

A recommender usually chases one question: “Which product will this customer buy?” NBA asks a wider one: “What’s the most valuable thing we can do for this customer, on this channel, at this moment?”

The difference comes from folding in channel and timing too. The same offer that flops over email can convert as an in-app nudge. NBA solves “what to recommend” and “where and when to send it” at once.

There’s another layer. A well-built NBA weighs the long-term effect of an action. An aggressive offer that earns a click today can annoy the customer and erode their value tomorrow. Getting that balance right is what separates NBA from a plain scoring engine.

The Decisioning Architecture Behind NBA

A mature NBA program rests on three layers that feed one another, not a single model. They run in sequence, each looking at a different question.

Layer 1: Propensity and uplift models

The first layer does the baseline scoring. Propensity models say “how inclined is this customer toward this action?” Uplift models go further and measure the actual effect: was the customer converting anyway, or did we genuinely make a difference?

This distinction is critical. Sending an offer to someone who would have converted regardless is burning budget. The real gain sits with the group that only acts because of the intervention.

Layer 2: Contextual bandits for real-time decisions

The second layer is a contextual bandit rooted in reinforcement learning. The system sees the customer’s context (behavior, device, timing), picks an action, and soon gets a reward signal: a click, a conversion, an app open.

The bandit’s power is in managing the explore-exploit tradeoff. It keeps tuning between repeating the known high-reward action and trying new options. So the system never locks onto one “winning” offer; it updates live as fresh signals arrive. These are common in tech companies’ recommendation systems today, and increasingly in enterprise marketing.

Layer 3: Agentic AI and compositional reasoning

The third layer is the newest. Foundation-model agents move past selecting a single action to chaining several steps. In a wealth scenario, an AI copilot handles the portfolio review and drafts the next-best-action suggestion, freeing the advisor to focus on the client’s real goals.

You have to think of the three together. A system on propensity scores alone stays static; one leaning only on the bandit drifts from long-term strategy. The value is in the layers feeding each other.

How Does NBA Create Value in Banking?

In Türkiye most banks already use AI across fraud detection, virtual assistants and more. Personalization sits at the center of it. AI-driven systems can offer budgeting advice from spending habits and sense a credit need weeks before the customer notices.

The gains organizations see from NBA don’t come from a single campaign; they come from a continuously learning decisioning layer that gets a little sharper with every new reward signal.

But success hinges largely on data. Real-time decisions need customer signals streaming instantly, models that are observable and retrainable, and a cleanly captured action-reward loop. The real bottleneck in most NBA projects isn’t model accuracy; it’s whether the data feeding the decision shows up on time and reliably.

Where Should You Start?

The move to NBA doesn’t happen in one step. The healthiest path starts narrow: one channel, one measurable action, a clear reward definition. Once the propensity and uplift layer is solid, you add the real-time contextual bandit layer; agentic structures come online as the infrastructure matures.

At GTech, our Symphony Sense and Analytics solutions help banks build this decisioning infrastructure end to end. The right next action only takes on meaning once it sits on the right data foundation.

Sources

CDP.com: Next Best Action
BCG: The Science Behind Next-Best Action Programs
Braze: Contextual Bandits
Backbase: AI in banking 2026

Prepared by: GTech Data Science Team

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That’s the textbook version, and it’s also where most articles on this topic stop. The formula is simple enough to teach in five minutes. Applying it to a real customer base, especially in banking or fintech, is where it gets interesting.

What Is Customer Lifetime Value?

CLV answers a question that quarterly revenue can’t: is this customer actually worth what it cost to win them? A customer who buys once for $200 looks identical to a customer who’ll spend $50 a month for four years, right up until you calculate CLV. Then the second one is worth roughly twelve times more.

This is why CLV sits next to customer acquisition cost (CAC) in almost every growth conversation. CAC tells you what you spent. CLV tells you what you got back. Neither number means much without the other.

The Basic CLV Formula

The standard version of the formula has three inputs:

CLV = Average Purchase Value × Purchase Frequency × Customer Lifespan

Here’s what that looks like with real numbers:

Multiply average purchase value by frequency and you get annual customer value, $320 in this case. Multiply that by lifespan and you get CLV. It’s the same logic e-commerce, SaaS, and retail businesses have used for years, and it’s a fine starting point.

Where the Basic Formula Falls Apart

The formula assumes every customer behaves like an average customer. Real customers don’t. Someone who buys once a year for a decade and someone who buys weekly for six months can produce the same average purchase frequency while representing completely different value and risk.

It also treats customer lifespan as a known number, when it’s actually the hardest part to estimate. And it ignores the time value of money entirely. A dollar of profit five years from now isn’t worth the same as a dollar today, but the basic formula doesn’t discount future cash flows, so it tends to overstate CLV for long relationships.

For a bank or fintech, there’s a bigger problem. A checking account customer doesn’t have one purchase frequency. They have deposits, withdrawals, a mortgage inquiry, a credit card application, and a support call, all in different rhythms, sometimes years apart. The basic formula was built for single-product, roughly regular purchase patterns. Banking relationships are neither.

Predictive CLV Models: A More Accurate Approach

This is the part that separates a marketing 101 explanation from something a data team can actually put into production.

BG/NBD and Gamma-Gamma

The Beta-Geometric/Negative Binomial Distribution model, usually shortened to BG/NBD, was introduced by Peter Fader, Bruce Hardie, and Ka Lok Lee in 2005 as an easier-to-implement alternative to an older model called Pareto/NBD. Instead of assuming a fixed purchase frequency, it treats each customer’s buying pattern and dropout probability as randomly distributed across the customer base, then estimates the odds that a given customer is still “alive” and how many more transactions to expect from them.

BG/NBD only predicts transaction counts. A companion model called Gamma-Gamma estimates how much each of those future transactions is likely to be worth. Put the two together and you get a CLV estimate built on actual purchase history instead of a single company-wide average.

Both models were designed for non-contractual relationships, where there’s no subscription telling you when a customer has left. That happens to describe most retail banking relationships fairly well: a customer can simply stop transacting without ever formally closing anything.

Where Machine Learning Fits In

BG/NBD and Gamma-Gamma work well with limited data and are easy to explain to a non-technical stakeholder, which matters more than people admit. Machine learning models, gradient boosting and neural network approaches in particular, can outperform them when you have enough historical data and want to fold in variables the probabilistic models can’t handle directly: channel, product mix, credit score, branch location, campaign response history.

The tradeoff is the usual one. More variables and more flexibility, less interpretability. A churn model that flags a customer as high-risk is only useful if someone can explain why, especially in a regulated industry. This is part of a broader shift toward predictive analytics for financial services, where machine learning increasingly complements or replaces static, rule-based scoring.

CLV in Banking and Fintech Looks Different

Most CLV content is written for e-commerce and SaaS, where one customer generally has one product and one purchase pattern. Financial services customers rarely work that way. A single retail banking customer might hold a savings account, a mortgage, and a credit card, each with its own margin, risk profile, and churn behavior.

That’s part of why banks tend to build CLV into broader customer analytics rather than treating it as a standalone metric. GTech’s Symphony Analytics platform, for example, scores customers for attrition risk and clusters them by behavior rather than balance alone, which gets closer to a true lifetime value picture than a single average-purchase formula ever could. Bank of Kigali’s recent data backbone rollout is a useful real-world example: unifying credit, customer, and deposit data first, then building segmentation and personalization on top of it.

None of this requires abandoning CLV as a concept. It just means the “customer” in the formula is really a bundle of products, and the model needs to reflect that.

CLV vs. CAC: The Ratio That Actually Matters

A commonly cited rule of thumb is a 3:1 ratio between CLV and CAC, meaning a customer should be worth at least three times what it cost to acquire them. Below that, growth becomes expensive to sustain. Well above it, you’re probably underspending on acquisition and leaving growth on the table.

The ratio is more useful than either number alone. A CLV of $10,000 sounds great until you learn the CAC was $8,000.

How to Increase Customer Lifetime Value

Retention is the highest-leverage lever here, and the economics back that up more specifically than the industry-wide stat that gets thrown around usually suggests. The original research behind it, Reichheld and Sasser’s 1990 Harvard Business Review study, found that reducing the customer defection rate by just 5% raised profits by 85% in one bank’s branch network specifically, not as a universal law across every industry (Reichheld & Sasser, HBR 1990). For a bank thinking about CLV, that’s a more relevant number than the inflated “25-95% for everyone” version that circulates elsewhere.

Beyond retention, three levers move CLV in practice:

1. Cross-sell and next-best-action. Recommending the right product to the right customer at the right time, based on actual behavior rather than a generic segment, raises purchase frequency without raising acquisition cost.

2. Early churn detection. Catching an at-risk customer three months before they leave gives retention teams time to act. Catching them the week they close the account doesn’t.

3. Segment-specific personalization. Treating a high-frequency, low-margin customer the same as a low-frequency, high-margin one wastes marketing spend on both.

Frequently Asked Questions

What is a good customer lifetime value?

There’s no universal number. What matters is the CLV to CAC ratio, and a ratio below 3:1 usually signals a growth model that’s more expensive than it looks.

How is CLV different from LTV?

They’re the same metric. LTV is just the more common shorthand in SaaS and subscription businesses, while CLV shows up more in retail and financial services writing.

Can CLV predict churn?

Not directly, but the two are closely linked. Predictive CLV models like BG/NBD produce a “probability of being active” for each customer as a byproduct, which is effectively a churn signal.

How often should CLV be recalculated?

For businesses with fast-changing purchase behavior, quarterly is reasonable. For relationships that move slowly, like most banking products, annually is usually enough, with churn scoring running more frequently in between.

Prepared by: GTech Data Science Team

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Because it doesn’t match the textbook definition of “the customer closed their account,” silent attrition doesn’t show up as a loss in standard reporting. But the effect on revenue is real: balances shrink, transaction volume drops, and cross-sell opportunities disappear. This piece looks at what silent attrition actually is, why banks miss it, which signals expose it, and how those signals can be turned into a working scoring practice.

What Is Silent Attrition?

Explicit churn is a clean event: the customer closes the account, cancels the card, signs the form. It’s easy to measure because it’s a single, dated transaction that shows up in the system immediately and rolls straight into the monthly churn number.

Silent attrition works differently. The customer never formally ends the relationship; they just shrink it. The paycheck moves to another bank, but the old account stays open to hold a small balance. Investment products drift to a fintech app. Card spending shifts to a competitor’s card. What’s left is an account that looks active in the system but is economically hollowed out. Picture a customer who once held a paycheck account, a credit card, and a small investment account, and who over a couple of years ends up with nothing but a low-balance checking account left behind; this is an illustrative pattern, not a specific case, but it’s one that shows up repeatedly across industry sources.

The industry sometimes calls this “silent switching,” or simply silent churn; the term used most consistently in English-language sources is silent attrition. The common thread is always the same: the customer still counts as “active” in the system, while the economic relationship with the bank has already thinned out considerably. The account is open. The relationship, in practical terms, is not.

Explicit churn is a single event; silent attrition unfolds over months.

Why Do Banks Miss It?

The root of the problem is measurement. Most banks track churn with a single blended rate: accounts closed during a period divided by total accounts. The standard formula is: (customers lost during the period / total customers at the start of the period) x 100. That calculation only counts customers who actually closed an account; a customer whose balance has quietly drained away while the account stays open never enters the denominator.

That blended view also masks cohort-level differences. Younger customer segments evaluate their primary banking relationship far more actively than older cohorts, who tend to hold the aggregate number steady. The result: the dashboard says “everything is fine” while the bank’s most valuable growth segment is quietly slipping away.

The second issue is where the data sits. As long as an account stays open, most CRM and reporting systems keep flagging that customer as “active.” Signals like a declining balance, reduced transaction frequency, or recurring outbound transfers sit in separate tables; unless someone combines and interprets them, no alarm ever fires. The credit team looks at one dashboard, the digital channel team looks at another, branch operations looks at a third; none of them sees the full picture on its own.

The third issue is organizational. By definition, silent attrition belongs to no single department. It never reaches the retention team’s radar because the account never closes, and it’s rarely a sales priority because the balance shrinking isn’t dramatic enough to trigger urgency. That gap between teams is exactly what lets the problem grow unnoticed for years.

What Do the Numbers Say?

Industry data suggests that annual customer attrition in banking runs somewhere between 15% and 25%, with rates climbing to three times that for accounts open less than six months. This figure comes from the US market and only counts accounts that actually closed; if dormant accounts were factored in, the picture would likely look considerably worse.

The impact is easier to grasp with a concrete example: at a US bank with $50 billion in assets, every one-point increase in the attrition rate can translate into roughly $12 million in lost annual net interest income. Scaled down to a mid-sized bank in Turkey, the exact figures would differ, but the mechanism is identical: shrinking balances, falling interest income, lost cross-sell potential.

A single case described by a banking consultant makes the scale of the problem more concrete. At a $10 billion US institution, analysis found that 64% of customers held an account elsewhere in a product category the bank itself offered; more than 30% of that group had three or more deposit relationships at other institutions. Looking at transaction data, 37% of customers were making a recurring transfer (not a payment, an actual transfer) to a competing institution every month. That institution’s reported churn rate sat under 3%; its actual silent attrition was an order of magnitude larger. This is one institution’s own analysis, not a generalizable industry average, but the method it used is repeatable: scan existing product categories for external relationships and recurring outbound transfers.

Which Signals Actually Matter?

Catching silent attrition doesn’t require exotic technology; it requires asking the right questions of transaction data banks already collect. In practice, a handful of signals do most of the work.

Average balance trend. Not a single low month, but a sustained decline across several months, especially when it coincides with a paycheck or recurring deposit disappearing.

Recurring outbound transfers. Not bill payments or purchases, but transfers to the same external account at regular intervals. This behavior is one of the strongest indicators that money is now being held somewhere else.

Narrowing product usage. A customer who once used a credit card, an investment account, and a deposit account, now left with nothing but a dormant checking account.

Declining digital engagement. A drop in mobile app login frequency, or falling below a defined threshold (say, 60-90 days), becomes meaningful especially when it shows up alongside other signals.

Unresolved complaints. A drop in transaction volume shortly after a complaint can be an indirect sign that the complaint was never actually resolved.

Cohort-level breakdown. Younger, digitally-oriented segments showing switching-evaluation behavior at rates well above the blended average.

None of these signals is conclusive on its own. A balance can dip for a month, a customer can make one large one-off purchase, app usage can drop during a vacation. But a customer showing three or four of these signals at once has entered a risk profile that warrants active outreach, even if they haven’t technically met the formal definition of churn yet.

None of the six signals is conclusive alone; they need to be read together.

Churn Risk and Churn Rate Are Not the Same Thing

It’s worth clearing up a terminology mix-up here. Churn rate is a backward-looking metric: it reports the percentage of customers lost during a defined period. Churn risk is a forward-looking prediction: a probability score for whether a given customer is likely to churn in the future. This is exactly where the silent attrition discussion matters most: a bank that only tracks the backward-looking rate will never see a customer who hasn’t closed their account yet but is already at high risk. Moving risk earlier means turning the behavioral signals above into a score.

Is Graph Analytics Necessary?

Some industry commentary argues that catching silent attrition requires moving to graph analytics: modeling customer, product, channel, and interaction data as a relationship network to surface connections standard tabular analysis can’t see. For complex, multi-institution relationship patterns, that approach adds real value, particularly in commercial banking, where a single corporate client may hold complicated relationships across several banks at once.

But for most of retail banking, it isn’t a prerequisite. Most of the signals listed above can already be detected once existing transaction data is properly modeled in a data warehouse with the right behavioral indicators added, without standing up a separate graph database layer. The critical point is that the data stops sitting in scattered tables and gets consolidated into one consistent layer that’s scored regularly. Graph analytics can be a useful next step for very complex corporate customer networks, but it isn’t the starting point. For most banks, the priority should be consolidating the scattered signals already sitting inside the existing DWH/BI stack.

Turning It Into a Scoring Practice: Four Steps

Detecting signals and turning them into an operational process are two different things. In practice, a four-step approach tends to work.

The first step is data consolidation. Balance, transaction, digital channel, and complaint data need to be visible in one layer, under one consistent customer identity. This is usually the most time-consuming step, but also the most critical one, since nothing downstream works well on scattered data.

The second step is defining behavioral indicators. The six signals above are a starting point; every bank needs to calibrate its own thresholds (say, a 20% balance decline, or 90 days without a login) against its own customer base.

The third step is scoring and prioritization. Signals shouldn’t be evaluated one at a time; customers showing multiple signals simultaneously should move to the top of the priority list.

The fourth step is triggering action. Once a score crosses a defined threshold, that information needs to flow automatically into the CRM or campaign system; otherwise detection stays a report that never turns into an intervention.

Four steps that close the gap between detection and action.

A Short Scenario: What Scoring Looks Like in Practice

To see how the four steps above play out, walk through a fictional example: this is an illustrative scenario, not real customer data.

Take a customer we’ll call John. Three years ago he had a paycheck account, a credit card, and a small investment account. Over the last eight months, three things changed: his paycheck kept landing on schedule, but a fixed amount started moving out to another bank at the start of every month; his investment account balance dropped to zero; and his mobile app logins fell from about ten a month to two.

Taken individually, each of these changes has an innocent explanation: maybe he moved his investments elsewhere, maybe he’s doing more in-branch and less on the app. But taken together (the recurring outbound transfer, the narrowing product mix, the declining digital engagement), the picture points to John gradually shifting his economic relationship to another institution. His account is still open, his card still active; this would never trip the formal definition of churn. But once a scoring model combines these three signals and crosses a threshold, the retention team gets a chance to reach out with a retention offer while there’s still a relationship left to save; once the account actually closes, that chance is gone.

This example shows why signals need to be read together rather than one at a time. It also shows why automation isn’t optional: no analyst can manually scan thousands of customers’ transaction histories looking for this kind of pattern. That’s what a systematic scoring layer is for.

What Does This Mean for Turkish and Finnish Markets?

The rapid growth of digital banking and fintech alternatives in Turkey is raising the risk that deposit and spending relationships fragment. It’s now technically far easier for a customer to keep a paycheck account at their primary bank while shifting savings or investing into a separate app, which raises the odds that silent attrition becomes a more common pattern going forward.

In a market like Finland, where digital banking is far more mature, the picture reads almost in reverse: customers tend to stay with the same bank for decades, but maintaining relationships with multiple digital providers in parallel has also become normal. In both markets the common denominator is the same: an open account is no evidence of a healthy relationship. Unless the measurement methodology is built to catch that, both markets end up with the same blind spot.

What Should Banks Actually Do?

Detection alone isn’t enough; the signal has to turn into action. What seems to work in practice falls into four categories.

First, design loyalty and reward programs around engagement, not just balance. Rewarding a customer purely for “high balance” delays noticing a customer whose balance is already draining away.

Second, treat complaint and feedback channels as an early-warning system for unresolved issues. When a customer complains at a branch or through the call center, that’s usually the first visible moment of a silent attrition process already underway; if the issue isn’t resolved right there, the customer quietly drifts away instead.

Third, move reporting from a blended rate to cohort-level dashboards. Tracking younger, digitally-oriented, and newly-opened-account segments separately surfaces early warnings the blended average hides.

Fourth, shorten the gap between detection and action. Once a customer’s risk score rises, the relevant team needs that information within days, ideally in near-real time or on a weekly cycle; the longer that gap stretches, the smaller the window to intervene.

Closing Thoughts

Silent attrition is an invisible but very real source of revenue loss in banking. The scale of the problem may be larger than most institutions assume, because standard churn metrics are structurally blind to it. The fix isn’t a new category of technology; it’s a data layer that reads the transaction data banks already have with the right signals. GTech Symphony Analytics’ Predictive Churn Management module scores this kind of behavioral risk before an account closes, giving banks time to intervene proactively.

For a bank looking to manage silent attrition, the first move isn’t buying a new tool; it’s mapping where the existing data already sits. Balance, transaction, digital channel, and complaint data are already being collected; what’s usually missing is a shared layer where they talk to each other. Once that layer exists, the signals described above start flowing automatically, and retention teams can finally step in before an account closes rather than after.

Related GTech resource: Symphony Analytics: Predictive Churn Management

Sources

Parloa, 8 bank churn reduction strategies that work in 2026

Bank Director, Patching Your Leaky Bucket: How Banks Can Address Silent Churn (March 19, 2024)

The Financial Brand, How to Uncover Hidden Pain Points that Cripple Customer Retention (November 28, 2025)

Banking Transformed podcast (Evergreen Podcasts), The Silent Attrition Crisis in Banking (May 8, 2026)

PriceWeber, Silent Attrition: 5 Ways Primary Banks Can Combat the Threat (October 21, 2025)

The Financial Brand, Acting on ‘Silent Attrition’ Is Key to Customer Loyalty in Banking (April 24, 2023)

Prepared by: GTech Data Science Team

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The award was received by Mine Taşkaya, Founding Partner and CEO of GTech, and Managing Partner Gürhan Taşkaya at the ceremony held on 12 May 2026.

Expansion into East Africa

In line with its vision of carrying the deep banking expertise it has built in Turkey to global markets, GTech began operating in the East Africa region following Dubai and Baku. Work on the digital transformation of the region’s financial ecosystem has been decisive in the company’s regional growth.

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